This is an archived copy of the 2020-21 catalogue. To access the most recent version of the catalogue, please visit http://e-catalog.jhu.edu.
Department website: http://cs.jhu.edu/
Computing has grown to be pervasive throughout engineering, science, business, society, and entertainment. The availability of relatively inexpensive high performance computing capabilities, ubiquitous high speed and wireless networking, and mobile computing have powered a technology-driven restructuring of the way society and most professions now operate. Information, and its associated processing and transport, is the commodity upon which corporations are built and lives are improved. At the center of this revolution, making it happen, are those who study computer science.
There are two dimensions to the field of computer science that establish it as a unique area. CS can be viewed as a stand-alone discipline worthy of study unto itself, and/or as an empowering discipline to be studied in conjunction with other areas. Core CS careers include (but are not limited to) software design and development, computer systems engineering or administration, and information security. Application areas span a wide range of fields and disciplines such as robotics, medical and health informatics, scientific research, data analytics, entrepreneurship, gaming/entertainment, and business computing to name a few.
Because computer science is a highly diverse and broadly applied field, studies can proceed in many different directions. Accordingly, the undergraduate and graduate programs in the Department of Computer Science at Johns Hopkins are flexible curricula designed to accommodate a wide range of goals. A student at Johns Hopkins can pursue appropriately customized versions of the following computer science programs: minor, bachelor of science, bachelor of arts, masters of science in engineering, and doctor of philosophy. Most of this catalog section is devoted to details regarding these programs.
Computer science research laboratories are currently active in the following areas at Hopkins: algorithm design and analysis, human-computer interaction, machine learning, data intensive computing, health informatics, computational medicine, computer vision and image processing, computer graphics, geometric modeling, programming languages, natural language and speech processing, information retrieval, cryptography and information security, secure and robust systems, storage systems, high-performance and scientific computing, computational genomics, networks and distributed systems, stream processing, robotics, and computer-integrated surgical systems.
Additionally, interdisciplinary research centers in the university have heavy involvement by Computer Science faculty: the Information Security Institute (ISI), the Laboratory for Computational Sensing and Robotics (LCSR), the Center for Language and Speech Processing (CLSP), the Institute for Data Intensive Engineering and Science (IDIES), the Institute for Computational Medicine (ICM), the Center for Computational Biology (CCB), and the Malone Center for Engineering in Healthcare (MCEH). An important component of the educational process in the department is the opportunity for undergraduate and graduate student participation in the research programs of the faculty. In particular, original research in close association with individual faculty members is emphasized at the graduate level.
There are several closely related programs at the undergraduate and graduate levels which involve significant coursework and faculty involvement from the Department of Computer Science. The Laboratory for Computational Sensing and Robotics (LCSR) offers a minor in robotics and also a minor in computer integrated surgery through the Engineering Research Center for Computer Integrated Surgical Systems and Technology. Details of these programs may be found elsewhere in this catalog in the section pertaining to the Laboratory for Computational Sensing and Robotics. Undergraduates with a strong interest in system design and performance may elect to pursue a bachelor degree in computer engineering. This field of study includes course work in computer science, as well as electrical and computer engineering. Although jointly administered by both departments, specific goals and requirements of the computer engineering degree may be found in the catalog section pertaining to the Department of Electrical and Computer Engineering only.
At the graduate level, the LCSR offers a Master of Science in Engineering (M.S.E.) in Robotics, designed for students from a wide variety of engineering, scientific, and mathematical backgrounds to advance their interdisciplinary knowledge in robotics. Details of this program may be found in the LCSR section of this catalog, or on the web at www.lcsr.jhu.edu/MSE. Lastly, the Master of Science in Security Informatics (M.S.S.I.) is a specialized graduate program offered through the Information Security Institute (ISI) in the WSE. The field of security informatics is fundamentally based on information security and assurance technologies (hardware, software, and networks) as related to issues such as policy, management, privacy/trust, health care, and law, from both national and international perspectives. Interested students can obtain detailed information regarding the M.S.S.I. online at https://isi.jhu.edu or in the ISI section of this catalog.
The CS department has shared programs with the Johns Hopkins University Information Security Institute (ISI) and the Department of Applied Mathematics.
ISI and CS have a Dual Masters Program (DMP) combining the Master of Science in Security Informatics (MSSI) and the (MSE CS). The Master of Science in Security Informatics (MSSI) is a specialized graduate program offered through ISI. The field of security informatics is fundamentally based on information security and assurance technologies (hardware, software, and networks) as related to issues such as policy, management, privacy/trust, health care, and law, from both national and international perspectives. Interested students can obtain detailed information regarding the M.S.S.I. online at https://isi.jhu.edu or in the ISI section of this catalog.
The Data Science program is a joint program between computer science and applied mathematics that aims to produce the next generation of leaders in data science by emphasizing mastery of the skills needed to translate real-world data-driven problems in mathematical ones, and then solving these problems by using a diverse collection of scientific tools.
For additional information regarding the academic programs available in Computer Science, and the facilities provided, please consult the sections which follow, or the departmental website cs.jhu.edu.
Combined Undergraduate/Graduate Program
As early as the beginning of their junior year, qualified students may apply for admission to a combined bachelor’s/master’s program which combines a B.S. or B.A. degree (in any department) with a master of science in engineering degree in Computer Science. This program allows students to simultaneously pursue both an undergraduate and a graduate degree program of study. Generally, the combined B.S./M.S.E. or B.A./M.S.E. program is accomplished in five years, although some students take more or less time. Applicants are judged on the basis of their performance in courses and their letters of recommendation. Double counting of at most two courses is subject to current WSE and departmental policies. Students may not take a 601.3xx or 601.4xx course as an undergraduate and the corresponding 601.6xx course for the M.S.E. degree. Combined students will have a graduate faculty advisor in the Computer Science Department who must approve the courses to be applied toward the master’s degree. For information on the requirements of the M.S.E. degree, see the Graduate Programs tab on this page, or ask in the departmental office for the document that lists those requirements.
Facilities
The CS department is primarily housed in Malone Hall, a state of the art, open concept research facility. Additional department research space is located in the adjacent Hackerman Hall.
The general department computing facilities include numerous workstations and servers. Two undergraduate laboratories combine to provide approximately 24 Linux workstations. One of these is a collaboration room allowing students to work in a team-based environment, with several private breakout rooms as well. At the graduate level there is a Master's Lab consisting of a collaboration area and workstation area, both consisting of several Linux workstations. All Ph.D. students are assigned dedicated desks in their research labs.
Focused research laboratories have significant resources that provide greater specialization, including isolated networks of PCs for security studies, high-performance computing clusters, robots and computer vision systems, a mock operating room equipped with medical robots and imaging equipment, and more.
The general department computing facilities are tied together by our own LAN, and access to specialized hardware in other departments, labs, and institutions is available via the university intranet and the Internet. In addition, the university provides wireless access to the JHU intranet and the Internet, as well as server systems that provide e-mail accounts for all students.
For current course information and registration go to https://sis.jhu.edu/classes/
Courses
Genomic data is becoming available in large quantities, but understanding how genetics contributes to human disease and other traits remains a major challenge. Machine learning and statistical approaches allow us to automatically analyze and combine genomic data, build predictive models, and identify genetic elements important to disease and cellular processes. This course will cover current uses of statistical methods and machine learning in diverse genomic applications including new genomic technologies. Students will present and discuss current literature. Topics include personal genomics, integrating diverse genomic data types, new technologies such as single cell sequencing and CRISPR, and other topics guided by student interest. The course will include a project component with the opportunity to explore publicly available genomic data. Recommended Course Background: coursework in data science or machine learning.
Students will examine a variety of topics regarding policy, legal, and moral issues related to the computer science profession itself and to the proliferation of computers in all aspects of society, especially in the era of the Internet. The course will cover various general issues related to ethical frameworks and apply those frameworks more specifically to the use of computers and the Internet. The topics will include privacy issues, computer crime, intellectual property law -- specifically copyright and patent issues, globalization, and ethical responsibilities for computer science professionals. Work in the course will consist of weekly assignments on one or more of the readings and a final paper on a topic chosen by the student and approved by the instructor.
Area: Humanities
This course provides freshmen computer science majors with an introduction to the field and department. A variety of faculty members will provide a mix of historical context and current topics. Classes will be interactive, enabling students to think about and explore topics in a fun way, as well as get to know their classmates. CS non-freshmen and minors may enroll by permission only. Satisfactory/Unsatisfactory only.
This course introduces fundamental structured and object-oriented programming concepts and techniques, using Java, and is intended for all who plan to use computer programming in their studies and careers. Topics covered include variables, arithmetic operators, control structures, arrays, functions, recursion, dynamic memory allocation, files, class usage and class writing. Program design and testing are also covered, in addition to more advanced object oriented concepts including inheritance and exceptions as time permits. First-time programmers are strongly advised to take EN.600.108 concurrently in Fall/Spring semesters.
Area: Engineering
This course is intended for novice programmers, and must be taken in conjunction with EN.600.107. The purpose of this course is to give first-time programmers extra hands-on practice with guided supervision. Students will work in pairs each week to develop working programs, with checkpoints for each development phase. Must have familiarity with computers. Satisfactory/Unsatisfactory only.
Corequisite(s): EN.600.107[C]
Area: Engineering
This course teaches intermediate to advanced programming, using C and C++. (Prior knowledge of these languages is not expected.) We will cover low-level programming techniques, as well as object-oriented class design, and the use of class libraries. Specific topics include pointers, dynamic memory allocation, polymorphism, overloading, inheritance, templates, collections, exceptions, and others as time permits. Students are expected to learn syntax and some language specific features independently. Course work involves significant programming projects in both languages.
Prerequisite(s): AP Computer Science, or (C+ or better in EN.600.107/EN.601.107 OR EN.580.200 OR EN.500.112 OR EN.500.113 OR EN.500.114) or EN.500.132 OR EN.500.133 OR EN.500.134 or instructor permission.
Area: Engineering
This course covers the design and implementation of data structures including arrays, stacks, queues, linked lists, binary trees, heaps, balanced trees (e.g. 2-3 trees, AVL-trees) and graphs. Other topics include sorting, hashing, memory allocation, and garbage collection. Course work involves both written homework and Java programming assignments.
Prerequisite(s): C+ or better in EN.600.120 OR EN.601.220 OR EN.600.107 OR EN.601.107 OR EN.500.112 OR (EN.500.113 AND EN.500.132) OR (EN.500.114 AND EN.500.132) or by permission of instructor.
Area: Engineering, Quantitative and Mathematical Sciences
We study the design and performance of a variety of computer systems from simple 8-bit micro-controllers through 32/64-bit RISC architectures all the way to ubiquitous x86 CISC architecture. We'll start from logic gates and digital circuits before delving into arithmetic and logic units, registers, caches, memory, stacks and procedure calls, pipelined execution, super-scalar architectures, memory management units, etc. Along the way we'll study several typical instruction set architectures and review concepts such as interrupts, hardware and software exceptions, serial and other peripheral communications protocols, etc. A number of programming projects, frequently done in assembly language and using various processor simulators, round out the course. [Systems].
Prerequisite(s): EN.600.120/EN.601.220
Area: Engineering
This course is an introduction to the theory of computing. topics include design of finite state automata, pushdown automata, linear bounded automata, Turing machines and phrase structure grammars; correspondence between automata and grammars; computable functions, decidable and undecidable problems, P and NP problems, NP-completeness, and randomization.
Prerequisite(s): EN.550.171/EN.553.171 OR EN.553.172
Area: Engineering, Quantitative and Mathematical Sciences
A broad survey course in video game design (as opposed to mathematical game theory), covering artistic, technical, as well as sociological aspects of video games. Students will learn about the history of video games, archetypal game styles, computer graphics and programming, user interface and interaction design, graphical design, spatial and object design, character animation, basic game physics, plot and character development, as well as psychological and sociological impact of games. Students will design and implement an experimental video game in interdisciplinary teams of 3-4 students as part of a semester-long project. Section 1 requires technical skills, including at least one programming course (preferably 2 or more). Section 2 requires artistic skills, including at least one multimedia course (preferably 2 or more). Open to sophomores and above.
Corequisite(s): EN.601.256
Area: Engineering
A lab course in support of 601.255: Introduction to Video Game Design covering a variety of multi-media techniques and applications from image processing, through sound design, to 3D modeling and animation. See 601.255: Introduction to Video Game Design for details about enrolling.
Corequisite(s): EN.600.255
A full-stack JavaScript developer is a person who can build modern software applications using primarily the JavaScript programming language. Creating a modern software application involves integrating many technologies - from creating the user interface to saving information in a database and everything else in between and beyond. A full-stack developer is not an expert in everything. Rather, they are someone who is familiar with various (software application) frameworks and the ability to take a concept and turn it into a finished product. This course will teach you programming in JavaScript and introduce you to several JavaScript frameworks that would enable you to build modern web, cross-platform desktop, and native/hybrid mobile applications. A student who successfully completes this course will be on the expedited path to becoming a full-stack JavaScript developer.
Prerequisite(s): EN.601.220 OR EN.601.226;Students must not have taken or be concurrently enrolled in EN.601.421 or EN.601.621 Object Oriented Software Engineering.
Area: Engineering
This course will provide students with a rich development experience, focused on the design and implementation of user interfaces and mobile applications. A brief overview of human computer interaction will provide context for designing, prototyping and evaluating user interfaces. Students will invent their own mobile applications and implement them using the Android SDK, which is JAVA based. An overview of the Android platform and available technologies will be provided, as well as XML for layouts, and general concepts for effective mobile development. Students will be expected to explore and experiment with outside resources in order to learn technical details independently. There will also be an emphasis on building teamwork skills, and on using modern development techniques and tools.
Prerequisite(s): EN.600.120 AND EN.600.226
Area: Engineering
This course is a project-based introduction to working on successful projects in health care. In the first half of the term, students perform reading and homework assignments designed to introduce: (1) the context of health care delivery and health IT, (2) techniques to overcome challenges to conducting health care data analyses, and (3) techniques to design meaningful applications around health care data. In the second half of the term, students work in small groups to solve a real-world problem of their choosing. Includes exercises in written and oral communication and team building. [Oral starting 2019]
Prerequisite(s): (EN.600.120 OR EN.601.220) AND (EN.600.226 OR EN.601.226)
Area: Engineering
This is a project-based course focusing on the design and implementation of practical software systems. Students will work in small teams to design and develop useful open-source software products that support our communities. Students will be paired with community partners and will aim to develop software that can be used after the course ends to solve real problems facing those partners today. Instructors will connect with the community partners and determine viable project areas prior to the course start. Students will meet with their community partners to analyze the challenges in their project area, agree on a concrete target project outcome, and gather requirements for their project. Based on these requirements, students will design and implement open-source software systems. [Oral]
Prerequisite(s): EN.600.120/EN.601.220 AND EN.600.226/EN.601.226
Area: Engineering
Introduction to database management systems and database design, focusing on the relational and object-oriented data models, query languages and query optimization, transaction processing, parallel and distributed databases, recovery and security issues, commercial systems and case studies, heterogeneous and multimedia databases, and data mining. [Systems] (www.cs.jhu.edu/~yarowsky/cs415.html)
Prerequisite(s): EN.600.226/EN.601.226;Students may receive credit for only one of EN.600.315, EN.600.415, EN.601.315, EN.601.415, EN.601.615.
Area: Engineering
This course teaches how to design and implement protocols that enable processes to exchange information, cooperate, and coordinate efficiently in a consistent manner over a computer network. Topics include communication protocols, group communication, distributed databases, distributed operating systems, and security. [Systems]. Students may receive credit for EN.600.337 or EN.600.437 but not both.
Area: Engineering
This course covers fundamental topics related to operating systems theory and practice. Topics include processor management, storage management, concurrency control, multi-programming and processing, device drivers, operating system components (e.g., file system, kernel), modeling and performance measurement, protection and security, and recent innovations in operating system structure. Course work includes the implementation of operating systems techniques and routines, and critical parts of a small but functional operating system.
Prerequisite(s): EN.600.120/EN.601.220 AND EN.600.226/EN.601.226 AND EN.600.233/EN.601.229;Students may receive credit for only one of EN.600.318, EN.600.418, EN.601.318, EN.601.418, EN.601.618.
Area: Engineering
This course prepares the programmer to tackle the massive data sets and huge problem size of modern scientific and enterprise computing. Google and IBM have commented that undergraduate CS majors are unable to "break the single server mindset" (http://www.google.com/intl/en/ press/pressrel/20071008_ibm_univ.html). Students taking this course will abandon the comfort of serial algorithmic thinking and learn to harness the power of cutting-edge software and hardware technologies. The issue of parallelism spans many architectural levels. Even ``single server'' systems must parallelize computation in order to exploit the inherent parallelism of recent multi-core processors. The course will examine different forms of parallelism in four sections. These are: (1) massive data-parallel computations with Hadoop!; (2) programming compute clusters with MPI; (3) thread-level parallelism in Java; and, (4) GPGPU parallel programming with NVIDIA's Cuda. Each section will be approximately 3 weeks and each section will involve a programming project. The course is also suitable for undergraduate and graduate students from other science and engineering disciplines that have prior programming experience. [Systems]
Prerequisite(s): EN.600.226/EN.601.226 AND EN.600.233/EN.601.229;Students may receive credit for only one of EN.600.320, EN.600.420, EN.601.320, EN.601.420, EN.601.620.
Area: Engineering
Suppose you could simply write down a description of your problem, and let the computer figure out how to solve it. What notation could you use? What strategy should the computer then use? In this survey class, you'll learn to recognize when your problem is an instance of satisfiability, constraint programming, logic programming, dynamic programming, or mathematical programming (e.g., integer linear programming). For each of these related paradigms, you'll learn to reformulate hard problems in the required notation and apply off-the-shelf software that can solve any problem in that notation -- including NP-complete problems and many of the problems you'll see in other courses and in the real world. You'll also gain some understanding of the general-purpose algorithms that power the software. [Analysis]Students can only receive credit for EN.600.325 or EN.600.425, not both.
Area: Engineering
Introduction to compiler design, including lexical analysis, parsing, syntax-directed translation, symbol tables, run-time environments, and code generation and optimization. Students are required to write a compiler as a course project.[Systems] Co-listed with EN.601.428
Prerequisite(s): EN.600.120 AND EN.600.226;Students may receive credit for only one of EN.600.328, EN.600.428, EN.601.328, EN.601.428, EN.601.628.
Area: Engineering
How can we effectively use functional programming techniques to build real-world software? This course will primarily focus on using the OCaml programming language for this purpose. Topics covered include OCaml basics, modules, standard libraries, testing, quickcheck, build tools, functional data structures and efficiency analysis, monads, streams, and promises. Students will practice what they learn in lecture via functional programming assignments and a final project.
Prerequisite(s): EN.601.226 OR Instructor Permission
Area: Engineering
This course begins with reviewing basic knowledge of the World Wide Web, and then exploring the central defense concepts behind Web security, such as same-origin policy, cross-origin resource sharing, and browser sandboxing. It will cover the most popular Web vulnerabilities, such as cross-site scripting (XSS) and SQL injection, as well as how to attack and penetrate software with such vulnerabilities. Students will learn how to detect, respond, and recover from security incidents. Newly proposed research techniques will also be discussed. [Systems]
Prerequisite(s): (EN.600.226 OR EN.601.226) AND (EN.600.233 OR EN.601.229)
Area: Engineering
This course will use a project-based approach to introduce undergraduates to research in computational biology and genomics. During the semester, students will take a series of large data sets, all derived from recent research, and learn all the computational steps required to convert raw data into a polished analysis. Data challenges might include the DNA sequences from a bacterial genome project, the RNA sequences from an experiment to measure gene expression, the DNA from a human microbiome sequencing experiment, and others. Topics may vary from year to year. In addition to computational data analysis, students will learn to do critical reading of the scientific iterature by reading high-profile research papers that generated groundbreaking or controversial results. [Applications] Recommended Course Background: Knowledge of the Unix operating system and programming expertise in a language such as Perl or Python.
Area: Engineering
An intensive capstone design project experience in video game development. Students will work in groups of 4-8 on developing a complete video game of publishable quality. Teams will (hopefully) include programmers, visual artists, composers, and writers. Students will be mentored by experts from industry and academia. Aside from the project itself, project management and communication skills will be emphasized. Enrollment is limited to ensure parity between the various disciplines. [General] May involve travel to MICA. Junior or senior standing recommended.
Prerequisite(s): EN.600.255
Area: Engineering
Students may receive credit for EN.601.456 or EN.601.356, but not both. Lecture only version of EN.601.456 (no project). Recommended Course Background: EN.601.455 or instructor permission required.
Prerequisite(s): EN.601.455 or instructor permission.;Students may receive credit for either EN.601.356 orEN.601.456, but not both.
Area: Engineering
The world is full of text: webpages, emails, newspaper articles, tweets, medical records, and so on. The purpose of text is for people to convey knowledge to other people. This course focuses on how computers analyze large, potentially streaming, text collections to automatically discover knowledge on their own (and to help people better find it themselves). Lectures and assignments will cover relevant topics in automatic classification (applied machine learning), linguistics, high-performance computing, and systems engineering, working with software systems for automatic question answering, populating knowledge bases, and aggregate analysis of social media such as Twitter. [Applications]
Prerequisite(s): EN.600.120 AND EN.600.226
Area: Engineering
This course is an optional hands-on lab supplement for a few courses in the curriculum. It will provide tutorial support and practical experience for developing deep ML systems using PyTorch and TensorFlow, and may provide exposure to some other frameworks. It will also go into detail on practical methods for scalable learning on large data sets, and other more practical issues in setting up deep learning systems.
Corequisite(s): Students must also register for EN.601.482 OR EN.601.682 OR EN.601.765.
Area: Engineering
Advances in technology are driving a change in medicine, from personalized medicine to population health. Computers and information technology will be critical to this transition. We shall discuss some of the coming changes in terms of computer technology, including computer-based patient records, clinical practice guidelines, and region-wide health information exchanges. We will discuss the underlying technologies driving these developments - databases and warehouses, controlled vocabularies, and decision support.
Area: Engineering
This course is the second half of a two-course sequence and is a continuation of course EN.660.410.01, CS Innovation and Entrepreneurship, offered by the Center for Leadership Education (CLE). In this sequel course the student groups, directed by CS faculty, will implement the business idea which was developed in the first course and will present the implementations and business plans to an outside panel made up of practitioners, industry representatives, and venture capitalists. [General]
Prerequisite(s): EN.660.410
Area: Engineering
Topics covered will include application layer protocols (e.g. HTTP, FTP, SMTP), transport layer protocols (UDP, TCP), network layer protocols (e.g. IP, ICMP), link layer protocols (e.g. Ethernet) and wireless protocols (e.g. IEEE 802.11). The course will also cover routing protocols such as link state and distance vector, multicast routing, and path vector protocols (e.g. BGP). The class will examine security issues such as firewalls and denial of service attacks. We will also study DNS, NAT, Web caching and CDNs, peer to peer, and protocol tunneling. Finally, we will explore security protocols (e.g. TLS, SSH, IPsec), as well as some basic cryptography necessary to understand these. Grading will be based on hands-on programming assignments, homeworks and two exams. [Systems]
Prerequisite(s): (EN.600.226 OR EN.601.226) AND (EN.600.233 OR EN.601.229);Students may receive credit for only one of EN.600.344, EN.600.444, EN.601.414, EN.601.614.
Area: Engineering
Similar material as EN.601.315 covered in more depth for advanced undergraduates. Introduction to database management systems and database design, focusing on the relational and object-oriented data models, query languages and query optimization, transaction processing, parallel and distributed databases, recovery and security issues, commercial systems and case studies, heterogeneous and multimedia databases, and data mining. [Systems] (www.cs.jhu.edu/~yarowsky/cs415.html)
Prerequisite(s): EN.600.226/EN.601.226;Students may receive credit for only one of EN.600.315, EN.600.415, EN.601.315, EN.601.415, EN.601.615.
Area: Engineering
Graduate version of 601.317 Systems. Students may receive credit for 601.317 or 601.417 but not both. Recommended Course Background: EN.601.220, EN.601.226
Prerequisite(s): Students may receive credit for only one of 417/617;(EN.600.120 OR EN.601.220) AND (EN.600.226 OR EN.601.226)
Area: Engineering
Similar material as EN.601.318, covered in more depth. Intended for advanced undergraduate students. This course covers fundamental topics related to operating systems theory and practice. Topics include processor management, storage management, concurrency control, multi-programming and processing, device drivers, operating system components (e.g., file system, kernel), modeling and performance measurement, protection and security, and recent innovations in operating system structure. Course work includes the implementation of operating systems techniques and routines, and critical parts of a small but functional operating system.
Prerequisite(s): EN.600.120/EN.601.220 AND EN.600.226/EN.601.226 AND EN.600.233/EN.601.229;Students may receive credit for only one of EN.600.318, EN.600.418, EN.601.318, EN.601.418, EN.601.618.
Area: Engineering
Clouds host a wide range of the applications that we rely on today. In this course, we study common cloud applications, traffic patterns that they generate, critical networking infrastructures that support them, and core networking and distributed systems concepts, algorithms, and technologies used inside clouds. We will also study how today's application demand is influencing the network’s design, explore current practice, and how we can build future's networked infrastructure to better enable both efficient transfer of big data and low-latency requirements of real-time applications. The format of this course will be a mix of lectures, discussions, assignments, and a project designed to help students practice and apply the theories and techniques covered in the course. [Systems] Prerequisites: EN.601.226 or permission. Students can only receive credit for one of 601.419/619. Recommended: a course in operating systems, networks or systems programming.
Prerequisite(s): Students may earn credit for EN.601.419 or EN.601.619, but not both.;EN.601.226 (or EN.600.226) AND EN.601.414 or permission from the instructor.
Area: Engineering
More advanced version of EN.601.320. Students may receive credit for EN.601.320 or EN.601.420, but not both. Prerequisite: EN.601.226 and EN.601.229 Computer System Fundamentals.
Prerequisite(s): EN.600.226/EN.601.226 AND EN.600.233/EN.601.229;Students may receive credit for only one of EN.600.320, EN.600.420, EN.601.320, EN.601.420, EN.601.620.
Area: Engineering
This course covers object-oriented software construction methodologies and their application. The main component of the course is a large team project on a topic of your choosing. Course topics covered include object-oriented analysis and design, UML, design patterns, refactoring, program testing, code repositories, team programming, and code reviews. [Systems or Applications] (http://pl.cs.jhu.edu/oose/index.shtml)
Prerequisite(s): EN.600.120/EN.601.220 AND EN.600.226/EN.601.226;Students may receive credit for only one of EN.600.321, EN.600.421, EN.601.421, EN.601.621.
Area: Engineering
Studies show that testing can account for over 50% of software development costs. This course presents a comprehensive study of software testing, principles, methodologies, tools, and techniques. Topics include testing principles, coverage (graph coverage, logic coverage, input space partitioning, and syntax-based coverage), unit testing, higher-order testing (integration, system-level, acceptance), testing approaches (white-box, black-box, grey-box), regression testing, debugging, delta debugging, and several specific types of functional and non-functional testing as schedule/interest permits (GUI testing, usability testing, security testing, load/performance testing, A/B testing etc.). For practical topics, state- of-the-art tools/techniques will be studied and utilized. [Systems]
Prerequisite(s): EN.601.290 OR EN.601.421;Students can take EN.601.422 or EN.601.622, but not both.
Area: Engineering
Students can only receive credit for EN.600.325 or EN.600.425, not both. Graduate level version of EN.600.325. Recommended Course Background: EN.600.226, EN.600.271, AS.110.107/AS.110.109
Area: Engineering
Functional, object-oriented, and other language features are studied independent of a particular programming language. Students become familiar with these features by implementing them. Most of the implementations are in the form of small language interpreters. Some type checkers and a small compiler will also be written. The total amount of code written will not be overly large, as the emphasis is on concepts. The ML programming language is the implementation language used. [Analysis] Prerequisites include EN.601.226. No Freshmen or Sophomores.
Prerequisite(s): EN.601.226
Area: Engineering, Quantitative and Mathematical Sciences
This course is designed as a follow-on to Principles of Programming languages. It will cover a wide array of fundamental topics in programming languages, including advanced functional programming, the theory of inductive definitions, advanced operational semantics, advanced type systems, program analysis, program verification, theorem provers and SAT solvers. [Analysis]
Prerequisite(s): EN.601.426
Area: Engineering
Introduction to compiler design, including lexical analysis, parsing, syntax-directed translation, symbol tables, runtime environments, and code generation and optimization. Students are required to write a compiler as a course project. [Systems]
Prerequisite(s): EN.600.120/EN.601.220 AND EN.600.226/EN.601.226 AND EN.600.233/EN.601.229
Area: Engineering
This is a graduate level course studying the applications of combinatorics and graph theory in computer science. We will start with some basic combinatorial techniques such as counting and pigeon hole principle, and then move to advanced techniques such as the probabilistic method, spectral graph theory and additive combinatorics. We shall see their applications in various areas in computer science, such as proving lower bounds in computational models, randomized algorithms, coding theory and pseudorandomness. [Analysis]Recommended Course Background: probability theory and linear algebra
Prerequisite(s): EN.553.171 OR EN.553.172 OR EN.550.171; probability theory and linear algebra recommended.;Students may receive credit for only one of 430/630
Area: Engineering, Quantitative and Mathematical Sciences
This course concentrates on the design of algorithms and the rigorous analysis of their efficiency. topics include the basic definitions of algorithmic complexity (worst case, average case); basic tools such as dynamic programming, sorting, searching, and selection; advanced data structures and their applications (such as union-find); graph algorithms and searching techniques such as minimum spanning trees, depth-first search, shortest paths, design of online algorithms and competitive analysis. [Analysis]
Prerequisite(s): EN.600.226/EN.601.226 AND (EN.553.171/EN.550.171 OR EN.553.172/EN.550.170 OR EN.600.271/EN.601.231;Students may receive credit for only one of EN.600.363, EN.600.463, EN.601.433, EN.601.633.
Area: Engineering, Quantitative and Mathematical Sciences
The course emphasizes algorithmic design aspects, and how randomization can be a helpful tool. The topics covered include: tail inequalities, linear programming relaxation & randomized rounding, de-randomization, existence proofs, universal hashing, markov chains, metropolis and metropolis-hastings methods, mixing by coupling and by eigenvalues, counting problems, semi-definite programming and rounding, lower bound arguments, and applications of expanders. [Analysis] (www.cs.jhu.edu/~cs464) Recommended Course Background: Probability
Prerequisite(s): (( N.600.363 OR EN.600.463) OR (EN.601.433 OR EN.601.633)) AND (EN.550.310 OR EN.553.310 OR EN.553.311 OR EN.550.420 OR EN.550.620) or equivalent.;Students may receive credit for only one of EN.600.464, EN.600.664, EN.601.434, EN.601.634.
Area: Engineering, Quantitative and Mathematical Sciences
This course provides an introduction to approximation algorithms. Topics include vertex cover, TSP, Steiner trees, cuts, greedy approach, linear and semi-definite programming, primal-dual method, and randomization. Additional topics will be covered as time permits. There will be a final project. Students may receive credit for EN.601.435 or EN.601.635, but not both. [Analysis]
Prerequisite(s): EN.600.363 OR EN.601.433 OR EN.601.633 OR permission.
Area: Engineering, Quantitative and Mathematical Sciences
This course provides an introduction to algorithmic game theory: the study of games from the perspective of algorithms and theoretical computer science. There will be a particular focus on games that arise naturally from economic interactions involving computer systems (such as economic interactions between large-scale networks, online advertising markets, etc.), but there will also be broad coverage of games and mechanisms of all sorts. Topics covered will include a) complexity of computing equilibria and algorithms for doing so, b) (in)efficiency of equilibria, and c) algorithmic mechanism design. [Analysis]
Prerequisite(s): EN.600.363 OR EN.600.463 OR EN.601.433 OR EN.601.633
Area: Engineering, Quantitative and Mathematical Sciences
This course begins with reviewing basic knowledge of the World Wide Web, and then exploring the central defense concepts behind Web security, such as same-origin policy, cross-origin resource sharing, and browser sandboxing. It will cover the most popular Web vulnerabilities, such as cross-site scripting (XSS) and SQL injection, as well as how to attack and penetrate software with such vulnerabilities. Students will learn how to detect, respond, and recover from security incidents. Newly proposed research techniques will also be discussed. [Systems]
Prerequisite(s): (EN.601.226 OR EN.600.226) AND (EN.601.229 OR EN.600.233);Students may receive credit for only one of 340/440/640.
Area: Engineering
This course will introduce students to cryptocurrencies and the main underlying technology of Blockchains. The course will start with the relevant background in cryptography and then proceed to cover the recent advances in the design and applications of blockchains. This course should primarily appeal to students who want to conduct research in this area or wish to build new applications on top of blockchains. It should also appeal to those who have a casual interest in this topic or are generally interested in cryptography. Students are expected to have mathematical maturity. [Analysis]
Prerequisite(s): Students may recieve credit for only one of EN.600.451 OR EN.601.441 OR EN.601.641;EN.601.226 AND (EN.553.211 OR EN.553.310 OR EN.553.311 OR EN.560.348 OR EN.553.420)
Area: Engineering
Modern Cryptography includes seemingly paradoxical notions such as communicating privately without a shared secret, proving things without leaking knowledge, and computing on encrypted data. In this challenging but rewarding course we will start from the basics of private and public key cryptography and go all the way up to advanced notions such as zero-knowledge proofs, functional encryption and program obfuscation. The class will focus on rigorous proofs and require mathematical maturity. [Analysis]
Prerequisite(s): (EN.601.231 OR EN.600.271 OR EN.600.471) AND (EN.550.310 OR EN.553.310 OR EN.553.311 OR EN.550.331 OR EN.550.420 OR EN.553.420);Students may receive credit for only one of EN.600.442, EN.601.442, EN.601.642.
Area: Engineering, Quantitative and Mathematical Sciences
Lecture topics will include computer security, network security, basic cryptography, system design methodology, and privacy. There will be a heavy work load, including written homework, programming assignments, exams and a comprehensive final. The class will also include a semester-long project that will be done in teams and will include a presentation by each group to the class. [Applications] Recommended Course Background: A basic course in operating systems and networking, or permission of instructor.
Prerequisite(s): (EN.600.318/EN.601.318 OR EN.600.418/EN.601.418) OR (EN.600.344 OR EN.600.444/EN.601.414) AND (EN.600.233 OR EN.601.229);Students may receive credit for only one of EN.600.443, EN.601.443, EN.601.643.
Area: Engineering
This course focuses on communication security in computer systems and networks. The course is intended to provide students with an introduction to the field of network security. The course covers network security services such as authentication and access control, integrity and confidentiality of data, firewalls and related technologies, Web security and privacy. Course work involves implementing various security techniques. A course project is required. [Systems]
Prerequisite(s): EN.600.120 AND EN.600.226 AND ( EN.600.344 OR EN.600.444 ) or permission;Students may receive credit for only one of EN.600.424, EN.650.424, EN.601.444, EN.601.644.
Area: Engineering
This semester-long course will teach systems and cryptographic design principles by example: by studying and identifying flaws in widely-deployed cryptographic products and protocols. Our focus will be on the techniques used in practical security systems, the mistakes that lead to failure, and the approaches that might have avoided the problem. We will place a particular emphasis on the techniques of provable security and the feasibility of reverse-engineering undocumented cryptographic systems. [Systems]
Prerequisite(s): EN.600.226/EN.601.226 AND EN.600.233/EN.601.229;Students may receive credit for only one of EN.600.454, EN.601.445, EN.601.645.
Area: Engineering
Many of the world's largest and fastest-growing datasets are text, e.g. DNA sequencing data, web pages, logs and social media posts. Such datasets are useful only to the degree we can query, compare and analyze them. Here we discuss two powerful approaches in this area. We will cover sketching, which enables us to summarize very large texts in small structures that allow us to measure the sizes of sets and of their unions and intersections. This in turn allows us to measure similarity and find near neighbors. Second, we will discuss indexing --- succinct and compressed indexes in particular -- which enables us to efficiently search inside very long strings, especially in highly repetitive texts. The course will involve significant programming projects. [Analysis]
Prerequisite(s): EN.601.220 AND EN.601.226
Area: Engineering
Your genome is the blueprint for the molecules in your body. It's also a string of letters (A, C, G and T) about 3 billion letters long. How does this string give rise to you? Your heart, your brain, your health? This, broadly speaking, is what genomics research is about. This course will familiarize you with a breadth of topics from the field of computational genomics. The emphasis is on current research problems, real-world genomics data, and efficient software implementations for analyzing data. Topics will include: string matching, sequence alignment and indexing, assembly, and sequence models. Course will involve significant programming projects. [Applications]
Prerequisite(s): EN.600.120/EN.601.220 AND EN.600.226/EN.601.226;Students may receive credit for only one of EN.600.439, EN.600.639, EN.601.447, EN.601.647.
Area: Engineering
Genomic data has the potential to reveal causes of disease, novel drug targets, and relationships among genes and pathways in our cells. However, identifying meaningful patterns from high-dimensional genomic data has required development of new computational tools. This course will cover current approaches in computational analysis of genomic data with a focus on statistical methods and machine learning.Topics will include disease association, prediction tasks, clustering and dimensionality reduction, data integration, and network reconstruction. There will be some programming and a project component. [Applications]Prerequisites: EN.601.226 or other programming experience, probability and statistics, linear algebra or calculus.
Prerequisite(s): Students may receive credit for only one of EN.600.438, EN.600.638, EN.601.448, EN.601.648.
Area: Engineering
[Co-listed with AS.020.415] This course for advanced undergraduates includes classroom instruction in interdisciplinary research approaches and lab work on an independent research project in the lab of a Bloomberg Distinguished Professor and other distinguished faculty. Lectures will focus on cross-cutting techniques such as data visualization, statistical inference, and scientific computing. In addition to two 50-minute classes per week, students will commit to working approximately 3 hours per week in the lab of one of the professors. The student and professor will work together to schedule the research project. Students will present their work at a symposium at the end of the semester.
Area: Engineering
Same as EN.601.654, for undergraduate students. This course introduces students to the field of Augmented Reality. It reviews its basic definitions, principles and applications. It then focuses on Medical Augmented Reality and its particular requirements. The course also discusses the main issues of calibration, tracking, multi-modal registration, advance visualization and display technologies. Homework in this course will relate to the mathematical methods used for calibration, tracking and visualization in medical augmented reality. [Applications]
Prerequisite(s): EN.601.120 AND EN.601.226 AND (AS.110.201 OR EN.550.291);Students may receive credit for only one of EN.600.484, EN.600.684, EN.601.454, EN.601.654.
Area: Engineering
This course focuses on computer-based techniques, systems, and applications exploiting quantitative information from medical images and sensors to assist clinicians in all phases of treatment from diagnosis to preoperative planning, execution, and follow-up. It emphasizes the relationship between problem definition, computer-based technology, and clinical application and includes a number of guest lectures given by surgeons and other experts on requirements and opportunities in particular clinical areas. Recommended Course Background: EN.601.220, EN.601.457, EN.601.461, image processing.
Prerequisite(s): EN.600.226/EN.601.226 AND (AS.110.201 OR AS.110.212) or permission of the instructor.;Students may receive credit for only one of EN.600.445, EN.600.645, EN.601.455, EN.601.655.
Area: Engineering
This weekly lecture/seminar course addresses similar material to EN.601.445, but covers selected topics in greater depth. In addition to material covered in lectures/seminars by the instructor and other faculty, students are expected to read and provide critical analysis/presentations of selected papers in recitation sessions. Students taking this course are required to undertake and report on a significant term project under the supervision of the instructor and clinical end users. Typically, this project is an extension of the term project from EN.601.445, although it does not have to be. Grades are based both on the project and on classroom recitations. Students wishing to attend the weekly lectures as a 1-credit seminar should sign up for EN.600.452. [Applications]
Prerequisite(s): EN.601.445 or EN.601.645 or permisssion;Students may receive credit for only one of EN.600.446, EN.600.646, EN.601.456, EN.601.656.
Area: Engineering
This course introduces computer graphics techniques and applications, including image processing, rendering, modeling and animation. [Applications]
Prerequisite(s): EN.600.120/601.220 AND EN.600.226/601.226, and linear algebra or permission of instructor.;Students may receive credit for only one of EN.600.357, EN.600.457, EN.601.457, EN.601.657.
Area: Engineering, Quantitative and Mathematical Sciences
This course will provide an introduction to computational geometry. It will cover a number of topics in two- and three-dimensions, including polygon triangulations and partitions, convex hulls, Delaunay and Voronoi diagrams, arrangements, and spatial queries. Time-permitting, we will also look at kD-trees, general BSP-trees, and quadtrees. [Analysis]
Prerequisite(s): EN.600.120/EN.601.220 AND EN.600.226/EN.601.226 AND (EN.600.363 OR EN.600.463/EN.601.433 OR EN.601.633)
Area: Engineering, Quantitative and Mathematical Sciences
This course gives an overview of fundamental methods in computer vision from a computational perspective. Methods studied include: camera systems and their modeling, computation of 3-D geometry from binocular stereo, motion, and photometric stereo; and object recognition. Edge detection and color perception are covered as well. Elements of machine vision and biological vision are also included. [Applications]
Prerequisite(s): Probability & Statistics, Linear Algebra, and Computing - (EN.550.310 OR EN.550.311 OR (EN.550.420 AND EN.550.430) OR EN.560.348) AND (AS.110.201 OR AS.110.212 OR EN.553.291) AND ( EN.500.112 OR EN.500.113[ OR EN.500.114 OR EN.600.120/EN.601.220 OR EN.600.107/EN.601.107 OR EN.580.200[C] OR AS.250.205);Students may receive credit for only one of EN.600.361, EN.600.461, EN.600.661, EN.601.461, EN.601.661.
Area: Engineering, Quantitative and Mathematical Sciences
This course surveys the development of robotic systems for navigating in an environment from an algorithmic perspective. It will cover basic kinematics, configuration space concepts, motion planning, and localization and mapping. It will describe these concepts in the context of the ROS software system, and will present examples relevant to mobile platforms, manipulation, robotics surgery, and human-machine systems. [Analysis]
Prerequisite(s): EN.601.226 AND (AS.110.201 OR AS.110.212) AND ( EN.553.310 OR EN.553.420);Students may receive credit for only one of EN.600.336, EN.600.436, EN.600.636, EN.601.463, EN.601.663.
Area: Engineering
This course is recommended for scientists and engineers with a genuine curiosity about the fundamental obstacles in getting machines to perform tasks such as deduction, learning, planning and navigation. It covers methods for automated reasoning, automatic problem solversand planners, knowledge representation mechanisms, game playing, machine learning,and statistical pattern recognition. Strong programming skills are expected, as well asbasic familiarity with probability. Students intending to also take courses in machinelearning (e.g. 601.475/675, 601.476/676, 601.482/682) may find it beneficial to take thiscourse first, or concurrently. [Applications]
Prerequisite(s): EN.600.226/EN.601.226;Students may receive credit for only one of EN.600.335, EN.600.435, EN.601.464, EN.601.664.
Area: Engineering
This course is an in-depth overview of techniques for processing human language. How should linguistic structure and meaning be represented? What algorithms can recover them from text? And crucially, how can we build statistical models to choose among the many legal answers? The course covers methods for trees (parsing and semantic interpretation), sequences (finite-state transduction such as morphology), and words (sense and phrase induction), with applications to practical engineering tasks such as information retrieval and extraction, text classification, part-of-speech tagging, speech recognition and machine translation. There are a number of structured but challenging programming assignments. [Applications]
Prerequisite(s): EN.600.226/EN.601.226;Students may receive credit for only one of EN.600.465, EN.601.465, EN.601.665.
Area: Engineering
An in-depth, hands-on study of current information retrieval techniques and their application to developing intelligent WWW agents. Topics include a comprehensive study of current document retrieval models, mail/news routing and filtering, document clustering, automatic indexing, query expansion, relevance feedback, user modeling, information visualization and usage pattern analysis. In addition, the course explores the range of additional language processing steps useful for template filling and information extraction from retrieved documents, focusing on recent, primarily statistical methods. The course concludes with a study of current issues in information retrieval and data mining on the World Wide Web. Topics include web robots, spiders, agents and search engines, exploring both their practical implementation and the economic and legal issues surrounding their use. Recommended Course Background: EN.601.226
Prerequisite(s): EN.600.226
Area: Engineering
This course gives an overview of basic foundations and applications of human language technology, such as: morphological, syntactic, semantic, and pragmatic processing; machine learning; signal processing; speech recognition; speech synthesis; information retrieval; text classification; topic modelling; informationextraction; knowledge representation; machine translation; dialog systems; etc. [Applications]Pre-req: EN.601.226 Data Structures; knowledge of Python recommended.
Prerequisite(s): EN.601.226 OR EN.600.226
Area: Engineering
Google translate can instantly translate between any pair of over fifty human languages (for instance, from French to English). How does it do that? Why does it make the errors that it does? And how can you build something better? Modern translation systems learn to translate by reading millions of words of already translated text, and this course will show you how they work. The course covers a diverse set of fundamental building blocks from linguistics, machine learning, algorithms, data structures, and formal language theory, along with their application to a real and difficult problem in artificial intelligence.
Prerequisite(s): EN.600.226/EN.601.226 and prob/stat.;Students may receive credit for only one of EN.600.468, EN.601.468, EN.601.668.
Area: Engineering
Machine learning is subfield of computer science and artificial intelligence, whose goal is to develop computational systems, methods, and algorithms that can learn from data to improve their performance. This course introduces the foundational concepts of modern Machine Learning, including core principles, popular algorithms and modeling platforms. This will include both supervised learning, which includes popular algorithms like SVMs, logistic regression, boosting and deep learning, as well as unsupervised learning frameworks, which include Expectation Maximization and graphical models. Homework assignments include a heavy programming components, requiring students to implement several machine learning algorithms in a common learning framework. Additionally, analytical homework questions will explore various machine learning concepts, building on the pre-requisites that include probability, linear algebra, multi-variate calculus and basic optimization. Students in the course will develop a learning system for a final project. [Analysis or Applications]
Prerequisite(s): Linear Algebra, Probability, Statistics, Calc III, and Intro Computing/Programming - AS.110.202 AND (EN.550.310 OR EN.553.211 OR EN.553.310 OR EN.553.311 OR ((EN.550.420 OR EN.553.420) AND (EN.550.430 OR EN.553.430)) OR EN.560.348) AND (AS.110.201 OR AS.110.212 OR EN.553.291) AND (EN.500.112 OR EN.500.113 OR EN.500.114 OR (EN.601.220 OR EN.600.120) OR AS.250.205 OR EN.580.200 OR (EN.600.107 OR EN.601.107));Students may receive credit for only one of EN.600.475, EN.601.475, EN.601.675.
Area: Engineering
How can robots localize themselves in an environment when navigating? Which factors predict whether patients are at greatest-risk for complications in the hospital? Can we reconstruct the brain's "connectome" from fMRI data? Many such big data questions can be answered using the paradigm of probabilistic models in machine learning. This is the second course on machine learning which focuses on probabilistic graphical models. You will learn about directed and undirected graphical models, inference methods, sampling, structure learning algorithms, latent variables, and temporal models. There will be regular assignments, which include theory and some programming. Students will analyze real data for their final project, applying methods discussed in class and writing up a report of their results. [Analysis or Applications] Students may receive credit for EN.600.476 or EN.600.676, but not both.
Prerequisite(s): EN.600.475/EN.601.475 OR EN.600.675/EN.601.675 or equivalent.;Students may receive credit for only one of EN.600.476, EN.601.476, EN.601.676.
Area: Engineering, Quantitative and Mathematical Sciences
"Big data" is not necessarily "high quality data." Systematically missing records, unobserved confounders, and selection effects present in many datasets make it harder than ever to answer scientifically meaningful questions. This course will teach mathematical tools to help you reason about causes, effects, and bias sources in data with confidence. We will use graphical causal models, and potential outcomes to formalize what causal effects mean, describe how to express these effects as functions of observed data, and use regression model techniques to estimate them. We will consider techniques for handling missing values, structure learning algorithms for inferring causal directionality from data, and connections between causal inference and reinforcement learning. [Analysis] Pre-requisites: familiarity with the R programming language, multivariate calculus, basics of linear algebra and probability.
Prerequisite(s): EN.600.475/EN.601.475 OR (EN.550.310 OR EN.550.420) AND AS.110.202 or permission of instructor.;Students may receive credit for only one of EN.600.477, EN.600.677, EN.601.477, EN.601.677.
Area: Engineering, Quantitative and Mathematical Sciences
Often the success of a machine learning project depends on the choice of features used. Machine learning has made great progress in training classification, regression and recognition systems when "good" representations, or features, of input data are available. However, much human effort is spent on designing good features which are usually knowledge-based and engineered by domain experts over years of trial and error. A natural question to ask then is "Can we automate the learning of useful features from raw data?" Representation learning algorithms such as principal component analysis aim at discovering better representations of inputs by learning transformations of data that disentangle factors of variation in data while retaining most of the information. The success of such data-driven approaches to feature learning depends not only on how much data we can process but also on how well the features that we learn correlate with the underlying unknown labels (semantic content in the data). This course will focus on scalable machine learning approaches for learning representations from large amounts of unlabeled, multi-modal, and heterogeneous data. We will cover topics including deep learning, multi-view learning, dimensionality reduction, similarity-based learning, and spectral learning. Students may receive credit for 600.479 or 600.679 but not both. [Analysis or Applications] Required course background: machine learning or basic probability and linear algebra.
Area: Engineering
Optimization is at the heart of machine learning. Most machine learning problems can be posed as optimization problems. However, unlike mathematical optimization where the focus is on efficient algorithms for finding solutions with a high degree of accuracy as measured by optimality conditions, optimization for machine learning focuses on algorithms that are efficient and generalize well. In this course, we will focus on optimization for problems that arise in machine learning, design and analysis of algorithms for solving these problems, and the interplay of optimization and machine learning. The coursework will include homework assignments and a final project focusing on applying optimization algorithms to real world machinelearning problems. [Analysis or Applications]
Prerequisite(s): EN.601.475[ OR ( EN.553.310 OR ( EN.553.420 AND EN.553.430 ) AND AS.110.201 AND AS.110.202 );Students may receive credit for only one of EN.601.481/681.
Area: Engineering, Quantitative and Mathematical Sciences
Deep learning (DL) has emerged as a powerful tool for solving data-intensive learning problems such as supervised learning for classification or regression, dimensionality reduction, and control. As such, it has a broad range of applications including speech and text understanding, computer vision, medical imaging, and perception-based robotics. The goal of this course is to introduce the basic concepts of deep learning (DL). The course will include a brief introduction to the basic theoretical and methodological underpinnings of machine learning, commonly used architectures for DL, DL optimization methods, DL programming systems, and specialized applications to computer vision, speech understanding, and robotics. Students will be expected to solve several DL problems on standardized data sets, and will be given the opportunity to pursue team projects on topics of their choice. [Applications]
Prerequisite(s): EN.601.226 AND (AS.110.201 OR AS.110.212 OR EN.553.291) AND (EN.553.310 OR EN.553.311 OR EN.553.420 OR EN.553.420); Python recommended.
Area: Engineering
The course gives an introduction to computational models of the mammalian visual cortex. It covers topics in low-, mid-, and high-level vision. It briefly discusses the relevant evidence from anatomy, electrophysiology, imaging (e.g., fMRI), and psychophysics. It concentrates on mathematical modelling of these phenomena taking into account recent progress in probabilistic models of computer vision and developments in machine learning, such as deep networks. [Applications or Analysis]. Programming experience (Python preferred).
Prerequisite(s): AS.110.106 OR AS.110.108
Area: Quantitative and Mathematical Sciences
The field of artificial intelligence (AI) has recently seen a substantial increase in popularity, largely fueled by the successes of training deep neural networks that achieve state-of-the-art performance in a large variety of problems. These successes are not limited to academic benchmarks but have started to impact our everyday lives in the form of products such as Google Lens, Amazon Alexa, and Tesla Autopilot. In order for such AI systems to succeed we must consider its impact on everyday life, its overall capabilities and performance, and the effectiveness of the human-AI interaction. The importance of harmonic interplay between all these components is dramatically highlighted by recent catastrophic events in road transport and aviation. In this project-based course you will work in teams of 3-5 students to 1) Identify a need with high-impact implications on everyday life; 2) Conceptualize and design an AI system targeting this need, and 3) Develop the AI system by refining a demo-able prototype based on feedback received during course presentations. Required course background: (EN.601.475/675 or EN.601.464/664 or EN.601.482/682) and Python programming. Recommended: 601.290 or 601.454/654 or 601.490/690 or 601.491/691 (experience with human computer interface design).
Prerequisite(s): (EN.601.475 OR EN.601.675) OR ( EN.601.464 OR EN.601.664) OR (EN.601.482 OR EN.601.682)
Area: Engineering
This course will introduce probabilistic modeling and information theory applied to biological sequence analysis, focusing on statistical models of protein families, alignment algorithms, and models of evolution. Topics will include probability theory, score matrices, hidden Markov models, maximum likelihood, expectation maximization and dynamic programming algorithms. Homework assignments will require programming in Python. Foundations of Computational Biology I is not a prereq. [Analysis] Co-listed with EN.580.488. Recommended Course Background: math through linear algebra and differential equations, at least one prob/stat course, EN.580.221 or equivalent, EN.600.226 or equivalent.
Area: Engineering, Natural Sciences
This course is designed to introduce undergraduate and graduate students to design techniques and practices in human-computer interaction (HCI), the study of interactions between humans and computing systems. Students will learn design techniques and evaluation methods, as well as current practices and exploratory approaches, in HCI through lectures, readings, and assignments. Students will practice various design techniques and evaluation methods through hands-on projects focusing on different computing technologies and application domains. This course is intended for undergraduate and graduate students in Computer Science/Cognitive Science/Psychology. Interested students from different disciplines should contact the instructor before enrolling in this course. [Applications]Recommended Background: Basic programming skills.
Prerequisite(s): Students can receive credit for either EN.601.490 or EN.601.690, but not both.
Area: Engineering
This course is designed to introduce advanced students to research methods and topics in human-robot interaction (HRI), an emerging research area focusing on the design and evaluation of interactions between humans and robotic technologies. Students will (1) learn design principles for building and research methods of evaluating interactive robot systems through lectures, readings, and assignments, (2) read and discuss relevant literature to gain sufficient knowledge of various research topics in HRI, and (3) work on a substantial project that integrates the principles, methods, and knowledge learned in this course. [Applications]
Prerequisite(s): EN.601.220/EN.600.120 AND EN.601.226/EN.600.226
Area: Engineering
This weekly lecture/seminar course addresses similar material to 600.455, but covers selected topics in greater depth. In addition to material covered in lectures/seminars by the instructor and other faculty, students are expected to read and provide critical analysis/presentations of selected papers in recitation sessions. Students taking this course are required to undertake and report on a significant term project in teams of at least 3 students, under the supervision of the instructor and clinical end users. Typically, this project is an extension of the term project from 600.455, although it does not have to be. Grades are based both on the project and on classroom recitations. Students who prefer to do individual projects must register for EN.601.456 instead. [Applications, Oral]
Prerequisite(s): Students may receive credit for only one of EN.601.456, EN.601.496, OR EN.601.656;EN.601.455 OR EN.601.655 or permission
Area: Engineering
An applications-oriented, computer science project done under the supervision and with the sponsorship of a faculty member in the Department of Computer Science. Computer Science Workshop provides a student with an opportunity to apply theory and concepts of computer science to a significant project of mutual interest to the student and a Computer Science faculty member. Permission to enroll in CSW is granted by the faculty sponsor after his/her approval of a project proposal from the student. Interested students are advised to consult with Computer Science faculty members before preparing a Computer Science Workshop project proposal.
Prerequisite(s): You must request Independent Academic Work using the Independent Academic Work form found in Student Self-Service: Registration > Online Forms.
Individual guided study for undergraduate students under the direction of a faculty member in the department. The program of study, including the credit to be assigned, must be worked out in advance between the student and the faculty member involved. Permission required.
Prerequisite(s): You must request Independent Academic Work using the Independent Academic Work form found in Student Self-Service: Registration > Online Forms.
Individual research for undergraduates under the direction of a faculty member in the department. The program of research, including the credit to be assigned, must be worked out in advance between the student and the faculty member involved. Permission required.
Prerequisite(s): You must request Independent Academic Work using the Independent Academic Work form found in Student Self-Service: Registration > Online Forms.
Individual work in the field with a learning component, supervised by a faculty member in the department. The program of study and credit assigned must be worked out in advance between the student and the faculty member involved. Students may not receive credit for work that they are paid to do. As a rule of thumb, 40 hours of work is equivalent to one credit. Permission required.
Prerequisite(s): You must request Independent Academic Work using the Independent Academic Work form found in Student Self-Service: Registration > Online Forms.
Independent research for undergraduates under the direction of a faculty member in the department. This course has a weekly research group meeting that students are expected to attend. The program of research, including the credit to be assigned, must be worked out in advance between the student and the faculty member involved.
Prerequisite(s): You must request Independent Academic Work using the Independent Academic Work form found in Student Self-Service: Registration > Online Forms.
The student will undertake a substantial independent research project under the supervision of a faculty member, potentially leading to the notation "Departmental Honors with Thesis" on the final transcript. Students are expected to enroll in both semesters of this course during their senior year. Project proposals must be submitted and accepted in the preceding spring semester (junior year) before registration. Students will present their work publicly before April 1st of senior year. They will also submit a first draft of their project report (thesis document) at that time. Faculty will meet to decide if the thesis will be accepted for honors. Computer science majors only. Students should have a 3.5 GPA in computer science courses at the end of their junior year and permission of faculty sponsor.
Prerequisite(s): You must request Independent Academic Work using the Independent Academic Work form found in Student Self-Service: Registration > Online Forms.
For computer science majors only, a continuation of EN.601.519. Recommended Course Background: EN.601.519
Prerequisite(s): You must request Independent Academic Work using the Independent Academic Work form found in Student Self-Service: Registration > Online Forms.
Prerequisite(s): You must request Independent Academic Work using the Independent Academic Work form found in Student Self-Service: Registration > Online Forms.;EN.600.445 or permission of instructor.
This course is the second half of a two-course sequence and is a continuation of course EN.660.410.01, CS Innovation and Entrepreneurship, offered by the Center for Leadership Education (CLE). In this sequel course the student groups, directed by CS faculty, will implement the business idea which was developed in the first course and will present the implementations and business plans to an outside panel made up of practitioners, industry representatives, and venture capitalists. [General]
Prerequisite(s): EN.660.410
Area: Engineering
This course considers intersystem communications issues. Topics covered include layered network architectures; the OSI model; bandwidth, data rates, modems, multiplexing, error detection/correction; switching; queuing models, circuit switching, packet switching; performance analysis of protocols, local area networks; and congestion control. Recommended Course Background: EN.601.220 and EN.601.229. Students can only receive credit for EN.600.344 or EN.601.414, not both.
Prerequisite(s): Students can only receive credit for EN.601.414 or EN.601.614, but not both.
Area: Engineering
Same material as 601.415, for graduate students. Introduction to database management systems and database design, focusing on the relational and object-oriented data models, query languages and query optimization, transaction processing, parallel and distributed databases, recovery and security issues, commercial systems and case studies, heterogeneous and multimedia databases, and data mining. [Systems] (www.cs.jhu.edu/~yarowsky/cs415.html) Recommended Course Background: EN.601.226
Prerequisite(s): Students may receive credit for only one of EN.600.315, EN.600.415, EN.601.315, EN.601.415, EN.601.615.
Area: Engineering
Graduate version of 601.317 Systems. Students may receive credit for 601.317 or 601.417 but not both. Recommended Course Background: EN.601.220, EN.601.226
Prerequisite(s): Students may receive credit for only one of 417/617
Area: Engineering
Same material as 601.418, for graduate students. This course covers fundamental topics related to operating systems theory and practice. Topics include processor management, storage management, concurrency control, multi-programming and processing, device drivers, operating system components (e.g., file system, kernel), modeling and performance measurement, protection and security, and recent innovations in operating system structure. Course work includes the implementation of operating systems techniques and routines, and critical parts of a small but functional operating system. [Systems] Recommended Course Background: 601.226 and 601.229.
Prerequisite(s): Students may receive credit for only one of EN.600.318, EN.600.418, EN.601.318, EN.601.418, EN.601.618.
Area: Engineering
Clouds host a wide range of the applications that we rely on today. In this course, we study common cloud applications, traffic patterns that they generate, critical networking infrastructures that support them, and core networking and distributed systems concepts, algorithms, and technologies used inside clouds. We will also study how today's application demand is influencing the network’s design, explore current practice, and how we can build future's networked infrastructure to better enable both efficient transfer of big data and low-latency requirements of real-time applications. The format of this course will be a mix of lectures, discussions, assignments, and a project designed to help students practice and apply the theories and techniques covered in the course. [Systems] Prerequisites: EN.601.226 or permission. Students can only receive credit for one of 601.419/619. Recommended: a course in operating systems, networks or systems programming.
Prerequisite(s): Students may earn credit for EN.601.419 or EN.601.619, but not both.
Area: Engineering
Graduate level version of EN.601.420. Recommended Course Background: EN.601.220 AND EN.601.229.
Prerequisite(s): Students may receive credit for only one of EN.601.320, EN.601.420, OR EN.601.620.
Area: Engineering
Same material as EN.601.421, for graduate students.This course covers object-oriented software construction methodologies and their application. The main component of the course is a large team project on a topic of your choosing. Course topics covered include object-oriented analysis and design, UML, design patterns, refactoring, program testing, code repositories, team programming, and code reviews. [Systems or Applications] Recommended Course Background: EN.601.226 and EN.601.220
Prerequisite(s): Students may receive credit for only one of EN.600.321, EN.600.421, EN.601.421, EN.601.621.
Area: Engineering
Studies show that testing can account for over 50% of software development costs. This course presents a comprehensive study of software testing, principles, methodologies, tools, and techniques. Topics include testing principles, coverage (graph coverage, logic coverage, input space partitioning, and syntax-based coverage), unit testing, higher-order testing (integration, system-level, acceptance), testing approaches (white-box, black-box, grey-box), regression testing, debugging, delta debugging, and several specific types of functional and non-functional testing as schedule/interest permits (GUI testing, usability testing, security testing, load/performance testing, A/B testing etc.). For practical topics, state- of-the-art tools/techniques will be studied and utilized. [Systems]
Prerequisite(s): EN.601.290 OR EN.601.421 OR EN.601.621;Students can only take EN.601.422 or EN.601.622, but not both.
Area: Engineering
Same material as EN.601.426, for graduate students. Functional, object-oriented, and other language features are studied independent of a particular programming language. Students become familiar with these features by implementing them. Most of the implementations are in the form of small language interpreters. Some type checkers and a small compiler will also be written. The total amount of code written will not be overly large, as the emphasis is on concepts. The ML programming language is the implementation language used. [Analysis]Required course background: EN.601.226.
Prerequisite(s): Students may only receive credit for EN.601.426 or EN.601.626, but not both
Area: Engineering, Quantitative and Mathematical Sciences
This course is designed as a follow-on to Principles of Programming Languages. It will cover a wide array of fundamental topics in programming languages, including advanced functional programming, the theory of inductive definitions, advanced operational semantics, advanced type systems, program analysis, program verification, theorem provers and SAT solvers.[Analysis]
Prerequisite(s): EN.601.426 OR EN.601.626
Introduction to compiler design, including lexical analysis, parsing, syntax-directed translation, symbol tables, runtime environments, and code generation and optimization. Students are required to write a compiler as a course project. [Systems]
Prerequisite(s): Students may receive credit for only one of EN.601.428 or 601.628.
Area: Engineering
This is a graduate level course studying the applications of combinatorics and graph theory in computer science. We will start with some basic combinatorial techniques such as counting and pigeon hole principle, and then move to advanced techniques such as the probabilistic method, spectral graph theory and additive combinatorics. We shall see their applications in various areas in computer science, such as proving lower bounds in computational models, randomized algorithms, coding theory and pseudorandomness. [Analysis]Recommended Course Background: probability theory and linear algebra
Prerequisite(s): Students may receive credit for only one of 430/630
Area: Engineering, Quantitative and Mathematical Sciences
This is a graduate-level course studying the theoretical foundations of computer science. Topics covered will be models of computation from automata to Turing machines, computability, complexity theory, randomized algorithms, inapproximability, interactive proof systems and probabilistically checkable proofs. Students may not take both EN.601.231 and EN.601.631, unless one is for an undergrad degree and the other for grad. [Analysis]Recommended Course Background: EN.553.171 or istructor permission.
Area: Engineering, Quantitative and Mathematical Sciences
Same material as EN.601.433, for graduate students.This course concentrates on the design of algorithms and the rigorous analysis of their efficiency. topics include the basic definitions of algorithmic complexity (worst case, average case); basic tools such as dynamic programming, sorting, searching, and selection; advanced data structures and their applications (such as union-find); graph algorithms and searching techniques such as minimum spanning trees, depth-first search, shortest paths, design of online algorithms and competitive analysis. [Analysis]
Prerequisite(s): Students may receive credit for only one of EN.600.363, EN.600.463, EN.601.433, EN.601.633.
Area: Engineering, Quantitative and Mathematical Sciences
Same material as 601.434, for graduate students. The course emphasizes algorithmic design aspects, and how randomization can be a helpful tool. The topics covered include: tail inequalities, linear programming relaxation & randomized rounding, de-randomization, existence proofs, universal hashing, markov chains, metropolis and metropolis-hastings methods, mixing by coupling and by eigenvalues, counting problems, semi-definite programming and rounding, lower bound arguments, and applications of expanders. [Analysis] (www.cs.jhu.edu/~cs464) Required course background: EN.600.363 or EN.601.433 or EN.601.633.
Prerequisite(s): Students may receive credit for only one of EN.600.464, EN.600.664, EN.601.434, EN.601.634.
Area: Engineering
Graduate version of EN.601.435. Recommended Background: EN.601.633 or equivalent. Students may receive credit for EN.601.435 or EN.601.635, but not both.
Same material as EN.601.436, for graduate students. This course provides an introduction to algorithmic game theory: the study of games from the perspective of algorithms and theoretical computer science. There will be a particular focus on games that arise naturally from economic interactions involving computer systems (such as economic interactions between large-scale networks, online advertising markets, etc.), but there will also be broad coverage of games and mechanisms of all sorts. Topics covered will include a) complexity of computing equilibria and algorithms for doing so, b) (in)efficiency of equilibria, and c) algorithmic mechanism design. [Analysis]
Prerequisite(s): Students may receive credit for EN.601.436 or EN.601.636, but not both.
Area: Engineering, Quantitative and Mathematical Sciences
This course begins with reviewing basic knowledge of the World Wide Web, and then exploring the central defense concepts behind Web security, such as same-origin policy, cross-origin resource sharing, and browser sandboxing. It will cover the most popular Web vulnerabilities, such as cross-site scripting (XSS) and SQL injection, as well as how to attack and penetrate software with such vulnerabilities. Students will learn how to detect, respond, and recover from security incidents. Newly proposed research techniques will also be discussed. [Systems]Required background: data structures and computer system fundamentals.
Prerequisite(s): Students may receive credit for only one of 340/440/640
Same as EN.601.441, for graduate students. This course will introduce students to cryptocurrencies and the main underlying technology of Blockchains. The course will start with the relevant background in cryptography and then proceed to cover the recent advances in the design and applications of blockchains. This course should primarily appeal to students who want to conduct research in this area or wish to build new applications on top of blockchains. It should also appeal to those who have a casual interest in this topic or are generally interested in cryptography. Students are expected to have mathematical maturity. Recommended Course Background: EN.601.226 AND (EN.553.310 OR EN.553.420) [Analysis]
Prerequisite(s): Students may receive credit for only one of EN.600.451 OR EN.601.441 OR EN.601.641
Area: Engineering
Same material as 601.442, for graduate students. Modern Cryptography includes seemingly paradoxical notions such as communicating privately without a shared secret, proving things without leaking knowledge, and computing on encrypted data. In this challenging but rewarding course we will start from the basics of private and public key cryptography and go all the way up to advanced notions such as zero-knowledge proofs, functional encryption and program obfuscation. The class will focus on rigorous proofs and require mathematical maturity. [Analysis] Required course background: EN.601.231 or EN.601.631.
Prerequisite(s): Students may receive credit for only one of EN.600.442, EN.601.442, EN.601.642.
Area: Engineering, Quantitative and Mathematical Sciences
Same material as 601.443, for graduate students. Lecture topics will include computer security, network security, basic cryptography, system design methodology, and privacy. There will be a heavy work load, including written homework, programming assignments, exams and a comprehensive final. The class will also include a semester-long project that will be done in teams and will include a presentation by each group to the class. [Applications] Recommended Course Background: A basic course in operating systems and networking, or permission of instructor.
Prerequisite(s): Students may receive credit for only one of EN.600.443, EN.601.443, EN.601.643.
Area: Engineering
Same material as 601.444, for graduate students. This course focuses on communication security in computer systems and networks. The course is intended to provide students with an introduction to the field of network security. The course covers network security services such as authentication and access control, integrity and confidentiality of data, firewalls and related technologies, Web security and privacy. Course work involves implementing various security techniques. A course project is required. [Systems]Recommended. Course Background: EN.601.220, EN.601.226 or equivalent
Prerequisite(s): Students may receive credit for only one of EN.600.454, EN.650.454, EN.601.445, EN.601.645.
Area: Engineering
Same material as 601.445, for graduate students. This semester-long course will teach systems and cryptographic design principles by example: by studying and identifying flaws in widely-deployed cryptographic products and protocols. Our focus will be on the techniques used in practical security systems, the mistakes that lead to failure, and the approaches that might have avoided the problem.We will place a particular emphasis on the techniques of provable security and the feasibility of reverse-engineering undocumented cryptographic systems. [Systems]
Prerequisite(s): Students may receive credit for EN.600.454/EN.601.445 or EN.601.645, but not both.
Area: Engineering
Many of the world's largest and fastest-growing datasets are text, e.g. DNA sequencing data, web pages, logs and social media posts. Such datasets are useful only to the degree we can query, compare and analyze them. Here we discuss two powerful approaches in this area. We will cover sketching, which enables us to summarize very large texts in small structures that allow us to measure the sizes of sets and of their unions and intersections. This in turn allows us to measure similarity and find near neighbors. Second, we will discuss indexing --- succinct and compressed indexes in particular -- which enables us to efficiently search inside very long strings, especially in highly repetitive texts. The course will involve significant programming projects. [Analysis]
Prerequisite(s): Students may receive credit for EN.601.446 or EN.601.646, but not both.
Same material as 601.447, for graduate students. Your genome is the blueprint for the molecules in your body. It's also a string of letters (A, C, G and T) about 3 billion letters long. How does this string give rise to you? Your heart, your brain, your health? This, broadly speaking, is what genomics research is about. This course will familiarize you with a breadth of topics from the field of computational genomics. The emphasis is on current research problems, real-world genomics data, and efficient software implementations for analyzing data. Topics will include: string matching, sequence alignment and indexing, assembly, and sequence models. Course will involve significant programming projects. [Applications]Recommended Course Background: EN.601.220 and EN.601.226
Prerequisite(s): Students may receive credit for only one of EN.600.439, EN.600.639, EN.601.447, EN.601.647.
Area: Engineering
Same material as EN.601.448, for graduate students. Genomic data has the potential to reveal causes of disease, novel drug targets, and relationships among genes and pathways in our cells. However, identifying meaningful patterns from high-dimensional genomic data has required development of new computational tools. This course will cover current approaches in computational analysis of genomic data with a focus on statistical methods and machine learning. Topics will include disease association, prediction tasks, clustering and dimensionality reduction, data integration, and network reconstruction. There will be some programming and a project component. [Applications]Recommended Course Background: EN.600.226 or other programming experience, probability and statistics, linear algebra or calculus.
Area: Engineering
This course introduces students to the field of Augmented Reality. It reviews its basic definitions, principles and applications. It then focuses on Medical Augmented Reality and its particular requirements. The course also discusses the main issues of calibration, tracking, multi-modal registration, advance visualization and display technologies. Homework in this course will relate to the mathematical methods used for calibration, tracking and visualization in medical augmented reality. Students may also be asked to read papers and implement various techniques within group projects. Recommended Course Background: EN.601.220, EN.601.226, and AS.110.201. [Applications]
Prerequisite(s): Students may receive credit for only one of EN.600.484, EN.600.684, EN.601.454, EN.601.654.
Same material as 601.455, for graduate students. This course focuses on computer-based techniques, systems, and applications exploiting quantitative information from medical images and sensors to assist clinicians in all phases of treatment from diagnosis to preoperative planning, execution, and follow-up. It emphasizes the relationship between problem definition, computer-based technology, and clinical application and includes a number of guest lectures given by surgeons and other experts on requirements and opportunities in particular clinical areas. [Applications] Recommended Course Background: intermediate programming in C/C++, EN.601.457, EN.601.461, image processing.
Prerequisite(s): Students may receive credit for only one of EN.600.445, EN.600.645, EN.601.455, EN.601.655.
Area: Engineering
Same material as EN.601.456, for graduate students. This weekly lecture/seminar course addresses similar material to EN.601.445, but covers selected topics in greater depth. In addition to material covered in lectures/seminars by the instructor and other faculty, students are expected to read and provide critical analysis/presentations of selected papers in recitation sessions. Students taking this course are required to undertake and report on a significant term project under the supervision of the instructor and clinical end users. Typically, this project is an extension of the term project from EN.601.445, although it does not have to be. Grades are based both on the project and on classroom recitations. Students wishing to attend the weekly lectures as a 1-credit seminar should sign up for EN.600.452. [Applications]
Prerequisite(s): EN.600.445/EN.601.455 OR EN.600.645/EN.601.655 OR permission of the instructor.;Students may receive credit for only one of EN.600.446, EN.600.646, EN.601.456, EN.601.656.
Same material as 601.457, for graduate students. This course introduces computer graphics techniques and applications, including image processing, rendering, modeling and animation. [Applications]Permission of instructor is required for students not satisfying a pre-requisite.Required course background: EN.601.220 (C++), EN.601.226, linear algebra.
Prerequisite(s): Students may receive credit for only one of EN.600.357, EN.600.457, EN.601.457, EN.601.657.
Area: Engineering, Quantitative and Mathematical Sciences
In this course we will look at fundamental techniques in geometry processing, including smoothing / sharpen-ing, parameterization, and (if time allows) vector fields. The course will begin with a review of the discretization of the underlying concepts from differential geometry (e.g. normals, curvature, Laplacian) before proceeding to specific applications
Prerequisite(s): EN.600.357 AND EN.600.457
This course will provide an introduction to computational geometry. It will cover a number of topics in two- and three-dimensions, including polygon triangulations and partitions, convex hulls, Delaunay and Voronoi diagrams, arrangements, and spatial queries. Time-permitting, we will also look at kD-trees, general BSP-trees, and quadtrees. [Analysis] Recommended Course Background: EN.601.220 AND EN.601.226 AND (EN.600.363 OR EN.601.433).
Prerequisite(s): Students may earn credit for EN.601.459 or EN.601.659, but not both.
Area: Engineering, Quantitative and Mathematical Sciences
Same material as 601.461, for graduate students. This course gives an overview of fundamental methods in computer vision from a computational perspective. Methods studied include: camera systems and their modeling, computation of 3-D geometry from binocular stereo, motion, and photometric stereo; and object recognition. Edge detection and color perception are covered as well. Elements of machine vision and biological vision are also included. [Applications] Recommended Course Background: intro programming, linear algebra, prob/stat.
Prerequisite(s): Students may receive credit for only one of EN.600.361, EN.600.461, EN.600.661, EN.601.461, EN.601.661.
Area: Engineering
Same material as EN.601.463, for graduate students. This course surveys the development of robotic systems for navigating in an environment from an algorithmic perspective. It will cover basic kinematics, configuration space concepts, motion planning, and localization and mapping. It will describe these concepts in the context of the ROS software system, and will present examples relevant to mobile platforms, manipulation, robotics surgery, and human-machine systems. [Analysis] Recommended Course Background: EN.600.226 and Linear Algebra and Probability
Prerequisite(s): Students may receive credit for only one of EN.600.336, EN.600.436, EN.600.636, EN.601.463, EN.601.663.
Area: Engineering
Same material as EN.601.464, for graduate students. This course is recommended for students, scientists, and engineers with a genuine curiosity about the fundamental obstacles in getting machines to perform tasks such as deduction, learning, planning and navigation. It covers methods for automated reasoning, automatic problem solvers and planners, knowledge representation mechanisms, game playing, machine learning, and statistical pattern recognition. Strong programming skills are expected, as well as basic familiarity with probability. Students intending to also take courses in machine learning (e.g. 601.475/675, 601.476/676, 601.482/682) may find it beneficial to take this course first, or concurrently.Prereq: EN.600.226; Recommended: linear algebra, prob/stat.
Prerequisite(s): Students may receive credit for only one of EN.600.335, EN.600.435, EN.601.464, EN.601.664.
Area: Engineering
Same material as 601.465, for graduate students. This course is an in-depth overview of techniques for processing human language. How should linguistic structure and meaning be represented? What algorithms can recover them from text? And crucially, how can we build statistical models to choose among the many legal answers? The course covers methods for trees (parsing and semantic interpretation), sequences (finite-state transduction such as morphology), and words (sense and phrase induction), with applications to practical engineering tasks such as information retrieval and extraction, text classification, part-of-speech tagging, speech recognition and machine translation. There are a number of structured but challenging programming assignments. [Applications]Recommended Course Background: EN.601.226
Prerequisite(s): Students may receive credit for only one of EN.600.465, EN.601.465, EN.601.665.
Area: Engineering
Same material as EN.601.466, for graduate students. An in-depth, hands-on study of current information retrieval techniques and their application to developing intelligent WWW agents. Topics include a comprehensive study of current document retrieval models, mail/news routing and filtering, document clustering, automatic indexing, query expansion, relevance feedback, user modeling, information visualization and usage pattern analysis. In addition, the course explores the range of additional language processing steps useful for template filling and information extraction from retrieved documents, focusing on recent, primarily statistical methods. The course concludes with a study of current issues in information retrieval and data mining on the World Wide Web. Topics include web robots, spiders, agents and search engines, exploring both their practical implementation and the economic and legal issues surrounding their use. [Applications]
Area: Engineering
This course gives an overview of basic foundations and applications of human language technology, such as: morphological, syntactic, semantic, and pragmatic processing; machine learning; signal processing; speech recognition; speech synthesis; information retrieval; text classification; topic modelling; informationextraction; knowledge representation; machine translation; dialog systems; etc. [Applications]Required Background: EN.601.226 Data Structures (not enforced for 6xx course number); knowledge of Python recommended.
Same material as 601.468, for graduate students. Google translate can instantly translate between any pair of over fifty human languages (for instance, from French to English). How does it do that? Why does it make the errors that it does? And how can you build something better? Modern translation systems learn to translate by reading millions of words of already translated text, and this course will show you how they work. The course covers a diverse set of fundamental building blocks from linguistics, machine learning, algorithms, data structures, and formal language theory, along with their application to a real and difficult problem in artificial intelligence. [Applications] Recommended Course Background: knowledge of python, prob/stat, EN.601.226; EN.601.465
Prerequisite(s): Students may receive credit for only one of EN.600.468, EN.601.468, EN.601.668.
Area: Engineering
Same material as 601.475, for graduate students. Machine learning is subfield of computer science and artificial intelligence, whose goal is to develop computational systems, methods, and algorithms that can learn from data to improve their performance. This course introduces the foundational concepts of modern Machine Learning, including core principles, popular algorithms and modeling platforms. This will include both supervised learning, which includes popular algorithms like SVMs, logistic regression, boosting and deep learning, as well as unsupervised learning frameworks, which include Expectation Maximization and graphical models. Homework assignments include a heavy programming components, requiring students to implement several machine learning algorithms in a common learning framework. Additionally, analytical homework questions will explore various machine learning concepts, building on the pre-requisites that include probability, linear algebra, multi-variate calculus and basic optimization. Students in the course will develop a learning system for a final project. [Applications or Analysis] Recommended Course Background: multivariable calculus, probability, linear algebra.
Prerequisite(s): Students may receive credit for only one of EN.600.475, EN.601.475, EN.601.675.
Area: Engineering
Same material as EN.601.476, for graduate students. How can robots localize themselves in an environment when navigating? Which factors predict whether patients are at greatest-risk for complications in the hospital? Can we reconstruct the brain's "connectome" from fMRI data? Many such big data questions can be answered using the paradigm of probabilistic models in machine learning. This is the second course on machine learning which focuses on probabilistic graphical models. You will learn about directed and undirected graphical models, inference methods, sampling, structure learning algorithms, latent variables, and temporal models. There will be regular assignments, which include theory and some programming. Students will analyze real data for their final project, applying methods discussed in class and writing up a report of their results. [Analysis or Applications] Recommended Background: EN.600.475 or EN.601.675 or equivalent. Students may receive credit for EN.600.476 or EN.600.676, but not both.
Prerequisite(s): Students may receive credit for only one of EN.600.476, EN.601.476, EN.601.676.
Area: Engineering, Quantitative and Mathematical Sciences
"Big data" is not necessarily "high quality data." Systematically missing records, unobserved confounders, and selection effects present in many datasets make it harder than ever to answer scientifically meaningful questions. This course will teach mathematical tools to help you reason about causes, effects, and bias sources in data with confidence. We will use graphical causal models, and potential outcomes to formalize what causal effects mean, describe how to express these effects as functions of observed data, and use regression model techniques to estimate them. We will consider techniques for handling missing values, structure learning algorithms for inferring causal directionality from data, and connections between causal inference and reinforcement learning. [Analysis] Pre-requisites: familiarity with the R programming language, multivariate calculus, basics of linear algebra and probability.
Prerequisite(s): Students may receive credit for only one of EN.600.477, EN.600.677, EN.601.477, EN.601.677.
Area: Engineering, Quantitative and Mathematical Sciences
Same material as EN.601.481, for graduate students. Optimization is at the heart of machine learning. Most machine learning problems can be posed as optimization problems. However, unlike mathematical optimization where the focus is on efficient algorithms for finding solutions with a high degree of accuracy as measured by optimality conditions, optimization for machine learning focuses on algorithms that are efficient and generalize well. In this course, we will focus on optimization for problems that arise in machine learning, design and analysis of algorithms for solving these problems, and the interplay of optimization and machine learning. The coursework will include homework assignments and a final project focusing on applying optimization algorithms to real world machine learning problems. [Analysis or Applications]Recommended Course Background: EN.601.475 OR ( EN.553.310 OR ( EN.553.420 AND EN.553.430 ) AND AS.110.201 AND AS.110.202 )
Prerequisite(s): Students may receive credit for only one of EN.601.481/681
Deep learning (DL) has emerged as a powerful tool for solving data-intensive learning problems such as supervised learning for classification or regression, dimensionality reduction, and control. As such, it has a broad range of applications including speech and text understanding, computer vision, medical imaging, and perception-based robotics. The goal of this course is to introduce the basic concepts of deep learning (DL). The course will include a brief introduction to the basic theoretical and methodological underpinnings of machine learning, commonly used architectures for DL, DL optimization methods, DL programming systems, and specialized applications to computer vision, speech understanding, and robotics. Students will be expected to solve several DL problems on standardized data sets, and will be given the opportunity to pursue team projects on topics of their choice. [Applications] Students should also consider taking EN.601.382 Deep Learning Lab as a supplement. Students may choose to skip the lab course if they already have a strong programming background and are comfortable learning on their own using online resources and tutorials. Required course background: Data structures, probability and linear algebra; numerical optimization and Python recommended.
Prerequisite(s): Students may receive credit for EN.601.482 or EN.601.682, but not both.
Area: Engineering
The course gives an introduction to computational models of the mammalian visual cortex. It covers topics in low-, mid-, and high-level vision. It briefly discusses the relevant evidence from anatomy, electrophysiology, imaging (e.g., fMRI), and psychophysics. It concentrates on mathematical modelling of these phenomena taking into account recent progress in probabilistic models of computer vision and developments in machine learning, such as deep networks. [Applications or Analysis]. Programming experience (Python preferred).
Area: Quantitative and Mathematical Sciences
The field of artificial intelligence (AI) has recently seen a substantial increase in popularity, largely fueled by the successes of training deep neural networks that achieve state-of-the-art performance in a large variety of problems. These successes are not limited to academic benchmarks but have started to impact our everyday lives in the form of products such as Google Lens, Amazon Alexa, and Tesla Autopilot. In order for such AI systems to succeed we must consider its impact on everyday life, its overall capabilities and performance, and the effectiveness of the human-AI interaction. The importance of harmonic interplay between all these components is dramatically highlighted by recent catastrophic events in road transport and aviation. In this project-based course you will work in teams of 3-5 students to 1) Identify a need with high-impact implications on everyday life; 2) Conceptualize and design an AI system targeting this need, and 3) Develop the AI system by refining a demo-able prototype based on feedback received during course presentations.Required course background: (EN.601.475/675 or EN.601.464/664 or EN.601.482/682) and Python programming. Recommended: 601.290 or 601.454/654 or 601.490/690 or 601.491/691 (experience with human computer interface design).
Prerequisite(s): (EN.601.475 OR EN.601.675) OR ( EN.601.464 OR EN.601.664) OR (EN.601.482 OR EN.601.682)
Area: Engineering
This course is designed to introduce undergraduate and graduate students to design techniques and practices in human-computer interaction (HCI), the study of interactions between humans and computing systems. Students will learn design techniques and evaluation methods, as well as current practices and exploratory approaches, in HCI through lectures, readings, and assignments. Students will practice various design techniques and evaluation methods through hands-on projects focusing on different computing technologies and application domains. This course is intended for undergraduate and graduate students in Computer Science/Cognitive Science/Psychology. Interested students from different disciplines should contact the instructor before enrolling in this course. [Applications]Recommended Background: Basic programming skills.
Prerequisite(s): Students can receive credit for either EN.601.490 or EN.601.690, but not both.
Area: Engineering
This course is designed to introduce graduate students to research methods and topics in human-robot interaction (HRI), an emerging research area focusing on the design and evaluation of interactions between humans and robotic technologies. Students will (1) learn design principles for building and research methods of evaluating interactive robot systems through lectures, readings, and assignments, (2) read and discuss relevant literature to gain sufficient knowledgeof various research topics in HRI, and (3) work on a substantial project that integrates the principles, methods, and knowledge learned in this course. [Applications] Required course background: EN.601.220 and EN.601.226.
Prerequisite(s): Students may receive credit for EN.601.491 or EN.601.691.
This is a graduate-level course on computer networks. It provides a comprehensive overview on advanced topics in network protocols and networked systems. The course will cover both classic papers on Internet protocols and recent research results. It will examine a wide range of topics, e.g., routing, congestion control, network architectures, datacenter networks, network virtualization, software-defined networking, and programmable networks, with an emphasize on core networking concepts and principles. The course will include lectures, paper discussions, programming assignments and a research project. Recommended Course Background: One undergraduate course in computer networks (e.g., EN.601.414/614 Computer Network Fundamentals or the equivalent), or permission of the instructor. The course assignments and projects assume students to be comfortable with programming.
The course explores the state of the art in distributed systems, networks and Internet research and practice, trying to see what it would take to push the envelop a step further. The course is conducted as a discussion group, where the professor and students brainstorm and pick interesting semester-long projects with high potential future impact. Example areas include robust scalable infrastructure (distributed datacenters, cloud networking, scada systems), real-time performance (remote surgery, trading systems), hybrid networks (mesh networks, 3-4G/Wifi/Bluetooth). Students should feel free to bring their own topics of interest and ideas. Recommended Course Background: a systems course (distributed systems, operating systems, computer networks, parallel programming) or permission of instructor.
Students will study advanced operating system topics and be exposed to recent developments in operating systems research. This course involves readings on classic and new papers. Topics include virtual memory management, synchronization and communication, file systems, protection and security, operating system structure and extension techniques, fault tolerance, and history and experience of systems programming. [Systems]
Prerequisite(s): EN.600.318 OR EN.600.418 OR EN.601.318 OR EN.601.418 OR EN.601.618
The advent of cloud computing has lead to an explosion of storage system and data analysis software, including NoSQL databases, bulk-synchronous processing, graph computing engines, and stream processing. This course will explore scale-out software architectures for data-processing tasks. It will examine the algorithms and data-structures that underlie scalable systems and look at how hardware and networking trends influence the design and deployment of cloud computing. Recommended Course Background: EN.601.320/420 or permission of instructor. [Systems]
Prerequisite(s): EN.600.320 OR EN.600.420 OR EN.601.620
Randomness is very useful in almost all areas of computer science, such as algorithms, distributed computing and cryptography. However, computers generally do not have access to truly uniform random bits. To deal with this, we rely on various pseudorandom objects to reduce either the quantity or the quality of the random bits needed. In this course, we will develop provably good pseudorandom objects for a variety of tasks. We will frequently require explicit combinatorial constructions. That is, we will want to efficiently and deterministically construct such objects. Along the way, we will also explore the close connections of such objects to many other areas in computer science and mathematics, such as graph theory, coding theory, complexity theory and arithmetic combinatorics. [Analysis] Recommended course background: EN.601.231 or EN.601.631, and probability.
This course will focus on advanced cryptographic topics with an emphasis on open research problems and student presentations.
Topics will vary from year to year, but will focus mainly on network perimeter protection, host-level protection, authentication technologies, intellectual property protection, formal analysis techniques, intrusion detection and similarly advanced subjects. Emphasis in this course is on understanding how security issues impact real systems, while maintaining an appreciation for grounding the work in fundamental science. Students will study and present various advanced research papers to the class. There will be homework assignments and a course project.
This reading and project based course will explore the latest research in the area of applied cryptography and cryptographic engineering. Topics covered will include zero knowledge, efficient multiparty computation, cryptocurrencies, and trusted computing hardware. Readings will be drawn from the latest applied cryptography and security conferences. The course will include both reading, critical analysis, presentations and a course programming project. [Analysis or Applications]
Prerequisite(s): EN.600.454 OR EN.601.445 OR EN.601.645 OR EN.600.442 OR EN.601.442 OR EN.601.642
Graduate level version of EN.600.438. [Applications] Recommended Course Background: EN.600.226 or other programming experience, probability and statistics, linear algebra or calculus. Students may receive credit for EN.600.438 or EN.600.638 but not both.
Area: Engineering
The goal of this course is to study the leading computational and quantitative approaches for comparing and analyzing genomes starting from raw sequencing data. The course will focus on human genomics and human medical applications, but the techniques will be broadly applicable across the tree of life. The topics will include genome assembly & comparative genomics, variant identification & analysis, gene expression & regulation, personal genome analysis, and cancer genomics. The grading will be based on assignments, a midterm & final exam, class presentations, and a significant class project. [Applications] Expected course background: familiarity with UNIX scripting and/or programming.
Public archives now contain petabytes of valuable but hard-to-analyze DNA sequencing data. Analyzing even small datasets is complicated by sequencing errors, differences between individuals, and the fragmentary nature of the the sequencing reads. In this course, we study recent algorithms and methods that seek to make sense of DNA sequencing datasets from small to very large. Topics covered will vary from year to year, but could include RNA sequencing data analysis, other functional genomics data analysis, metagenomics analysis, data compression, indexing, applications of streaming algorithms and sketch data structures, assembly, etc. There will be homework assignments and a course project. [Applications]
Genomic data is becoming available in large quantities, but understanding how genetics contributes to human disease and other traits remains a major challenge. Machine learning approaches allow us to automatically analyze and combine genomic data, build predictive models, and identify genetic elements important to disease and cellular processes. This course will cover uses of machine learning in diverse genomic applications. Students will present and discuss current literature. Topics include predicting disease risk from genomic data, integrating diverse genomic data types, gene network reconstruction, and other topics guided by student interest. The course will include a project component with the opportunity to explore publicly available genomic data. Recommended course background: coursework in data mining, machine learning. [Applications] Students may receive credit for 600.441 or 600.641, but not both.
In this course, we will study the Fourier Transform from the perspective of representation theory. We will begin by considering the standard transform defined by the commutative group of rotations in 2D and translations in two- and three-dimensions, and will proceed to the Fourier Transform of the non-commutative group of 3D rotations. Subjects covered will include correlation of images, shape matching, computation of invariances, and symmetry detection. Recommended Course Background: AS.110.201 and comfort with mathematical derivations.
This course surveys the development of robotic systems for navigating in an environment from an algorithmic perspective. It will cover basic kinematics, configuration space concepts, motion planning, and localization and mapping. It will describe these concepts in the context of the ROS software system, and will present examples relevant to mobile platforms, manipulation, robotics surgery, and human-machine systems. [Analysis]Formerly EN.600.436. Students may receive credit for only one of EN.600.336, EN.600.436 or EN.600.636. Recommended Course Background: EN.600.226, AS.110.106, and Prob/Stat.
This course surveys formal ingredients that are used to build structured models of character and word sequences. We will unpack recent deep learning architectures that consider various kinds of latent structure, and see how they draw on earlier work in structured prediction, dimensionality reduction, Bayesian nonparametrics, multi-task learning, etc. We will also examine a range of strategies used for inference and learning in these models. Students will beexpected to read recent papers and carry out a research project. [Applications or Analysis]
Prerequisite(s): EN.600.465/EN.601.465 or EN.601.665
Introduction to statistical methods of speech recognition (automatic transcription of speech) and understanding. The course is a natural continuation of EN.600.465 but is independent of it. Topics include elementary information theory, hidden Markov models, the Baum and Viterbi algorithms, efficient hypothesis search methods, statistical decision trees, the estimation-maximization (EM) algorithm, maximum entropy estimation and estimation of discrete probabilities from sparse data for acoustic and language modeling. Weekly assignments and several programming projects. Co-listed as EN.520.666. Recommended Course Background: EN.600.120 and EN.550.310 or equivalent, expertise in C or C++ programming
The overall objective of this course is for students to learn about state-of-the-art research in dialog systems, particularly focused on deep learning methods. Students will also learn how to read and navigate academic literature. The class will be centered around a 2-3 person project, with presentations/demos at the end of the semester. Students are expected to read, write, and review workshop-level academic papers. [Applications] Required course background: EN.601.467/667 or [(EN.601.475/675 or EN.601.482/682) and (EN.601.465/665 or EN.601.468/668 or EN.601.765)] or permission.
This course explores selected topics in the nature of event representations from the perspective of cognitive science, computer science, linguistics, and philosophy. These fields have developed a rich array of scientific theories about the representation of events, and how humans make inferences about them -- we investigate how (and if) such theories could be applied to current research topics and tasks in computational semantics such as inference from text, automated summarization, veridicality assessment, and so on. In addition to classic articles dealing with formal semantic theories, the course considers available machine-readable corpora, ontologies, and related resources that bear on event structure, such as WordNet, PropBank, FrameNet, etc.. The course is aimed to marry theory with practice: students with either a computational or linguistic background are encouraged to participate. [Applications]
This is a second graduate level course in machine learning. It will provide a formal and an in-depth coverage of topics at the interface of statistical theory and computational sciences. We will revisit popular machine learning algorithms and understand their performance in terms of the size of the data (sample complexity), memory needed (space complexity), as well as the overall computational runtime (computation or iteration complexity). We will cover topics including nonparametric methods, kernel methods, online learning and reinforcement learning, as well as introduce students to current topics in large-scale machine-learning and randomized projections. Topics will vary from year-to-year but the general focus would be on combining methodology with theoretical and computational foundations. [Analysis or Applications]
Students in the class will be asked to do assignments in Matlab. Matlab is typically easy to pick up if one is already familiar with a different programming language. Students are expected to be mathematically mature. One should have taken at least an introductory course in probability theory and linear algebra. Though not required, exposure to optimization or machine learning is recommended. Proficiency in at least one programming language is expected. When in doubt, send the instructor a copy of your transcript to see if the class is appropriate for you. Also, sit through the first few sessions and first homework to get a sense of fit. Requistes include Intro Prob/Stat, Linear Algebra and Intro Machine Learning as well as strong background in s.
This course will cover advanced topics on all areas of causal inference, including learning causal effects, path-specific effects, and optimal policies from data featuring biases induced by missing data, confounders, selection, and measurement error, techniques for generalizing findings to different populations, complex probabilistic models relevant for causal inference applications, learning causal structure from data, and inference under interference and network effects. The course will feature a final project which would involve either an applied data analysis problem (with a causal inference flavor), a literature review, or theoretical work. [Analysis] Recommended Background: EN.600.477/677 or EN.601.477/677
This course will focus on recent advances in machine learning. Topics will vary from year to year. The course will be project focused and involve presenting and discussing recent research papers.
Prerequisite(s): EN.600.475/EN.601.475 or EN.600.675/EN.601.675 or EN.600.775/EN.601.775 or EN.600.479/EN.601.479 or EN.600.679/EN.601.679 or EN.600.476/EN.601.476 or EN.600.676/EN.601.676 or permission.
In the era of data deluge, the development of methods for discovering structure in high-dimensional data is becoming increasingly important. This course will cover state-of-the-art methods from algebraic geometry, sparse and low-rank representations, and statistical learning for modeling and clustering high-dimensional data. The first part of the course will cover methods for modeling data with a single low-dimensional subspace, such as PCA, Robust PCA, Kernel PCA, and manifold learning techniques. The second part of the course will cover methods for modeling data with multiple subspaces, such as algebraic, statistical, sparse and low-rank subspace clustering techniques. The third part of the course will cover applications of these methods in image processing, computer vision, and biomedical imaging. Requistes include Linear Algebra, Optimization, and prior exposure to Machine I.
This is an advanced course on computer vision from a probabilistic and machine learning perspective. It covers techniques such as linear and non-linear filtering, geometry, energy function methods, markov random fields, conditional random fields, graphical models, probabilistic grammars, and deep neural networks. These are illustrated on a set of vision problems ranging from image segmentation, semantic segmentation, depth estimation, object recognition, object parsing, scene parsing, action recognition, and text captioning. [Analysis orApplications] Required course background: calculus, linear algebra (AS.110.201 or equiv.), probability and statistics (AS.553.311 or equiv.), and the ability to program in Python and C++. Background in computer vision (EN.601.461/661) and machine learning (EN.601.475) suggested but not required.
Required for all full-time CS PhD students. Recommended for MSE students.
Permission required. Independent research for masters or pre-dissertation PhD students.
Permission required. Individual study in an area of mutual interest to a graduate student and a faculty member in the department.
PhD students will gain valuable teaching experience, working closely with their assigned faculty supervisor. Successful completion of this course fulfills the PhD teaching requirement.(grad students) Permission req'd.
This course will explore current issues and research in computer science education. Topics will be drawn from literature, news items, and participant experience. Current faculty and students with interests in academic careers are encouraged to attend.
In this course, we will read, discuss and present classic papers and current research in computer networks. The topic coverage will vary each semester.
Area: Engineering
This course covers latest advances in the research of computer systems including operating systems, distributed system, mobile and cloud computing. Students will read and discuss recent research papers in top systems conferences. Each week, one student will present the paper and lead the discussion for the week. The focus topics covered in the papers vary semester to semester. Example topics include fault-tolerance, reliability, verification, energy efficiency, and virtualization.
Participants will read and discuss seminal and recent foundational research on cloud and networked systems.
This seminar course covers recent developments in the foundations of programming language design and implementation. Topics covered include type theory, process algebra, higher-order program analysis, and constraint systems. Students will be expected to present papers orally.
Seminar series in theoretical computer science. Topics include algorithms, complexity theory, and related areas of TCS. Speakers will be a mix of internal and external researchers, mostly presenting recently published research papers.
This course will explore algorithms and theoretical computer science with a focus on algorithms for massive data. Examples of topics include streaming algorithms, approximation algorithms, online algorithms. Students will be encouraged to select a paper and lead a discussion. External speakers will be invited to present current work as well. This course is a good opportunity for motivated students to learn modern algorithmic methods.Recommended Course Background: EN.601.433 or equivalent.
Area: Engineering
In this course students will read, discuss and present current research papers in applied cryptography. Topic coverage will vary each semester. Instructor approval required.
This weekly seminar will focus on research issues in medical image analysis, including imagesegmentation, registration, statistical modeling, and applications. It will also include selected topicsrelating to medical image acquisition, especially where they relate to analysis. The purpose of thecourse is to provide the participants with a thorough background in current research in these areas, as well as to promote greater awareness and interaction between multiple research groups withinthe University. The format of the course is informal. Students will read selected papers. All students will be assumed to have read these papers by the time the paper is scheduled for discussion. But individual students will be assigned on a rotating basis to lead the discussion on particular papers or sections of papers. Co-listed with En.520.746.
In this course we will review current research in computer graphics. We will meet for an hour once a week and one of the participants will lead the discussion for the week.
A reading group exploring important current research in the field and potentially relevant material from related fields. Enrolled students are expected to present papers and lead discussion.
Prerequisite(s): EN.601.465 OR EN.601.665
This weekly reading group will review current research and survey articles on the topics of computational semantics, statistical machine translation, and natural language generation. Enrolled students will present papers and lead discussions.
Students in this course will review, present, and discuss current research in machine translation. Permission of instructor.
This seminar is recommended for all students interested in data intensive computing research areas (e.g., machine learning, computer vision, natural language processing, speech, computational social science). The meeting format is participatory. Papers that discuss best practices and the state-of-the-art across application areas of machine learning and data intensive computing will be read. Student volunteers lead individual meetings. Faculty and external speakers present from time-to-time. Recommended Course Background: machine learning or permission of the instructor.
Cross Listed Courses
Applied and Computational Mathematics
Differential and integral calculus of functions of one independent variable. Topics include the basic analytic geometry of graphs of functions, and their limits, integrals and derivatives, including the Fundamental Theorem of Calculus. Also, some applications of the integral, like arc length and volumes of solids with rotational symmetry, are discussed. Applications to the physical sciences and engineering will be a focus of this course, as this course is designed to meet the needs of students in these disciplines. Course Note(s): Not for graduate credit. Not eligible for financial aid.
Differential and integral calculus. Includes analytic geometry, functions, limits, integrals and derivatives, polar coordinates, parametric equations, Taylor’s theorem and applications, infinite sequences and series. Some applications to the physical sciences and engineering will be discussed, and the course is designed to meet the needs of students in these disciplines.Prerequisite(s): 625.108 Calculus I Course Note(s): Not for graduate credit. Not eligible for financial aid.
Prerequisite(s): EN.625.108 Calculus I
Artificial Intelligence
Machine Learning (ML) is the art of solving a computation problem using a computer without an explicit program. ML is now so pervasive that various ML applications such as image recognition, stock trading, email spam detection, product recommendation, medical diagnosis, predictive maintenance, cybersecurity, etc. are constantly used by organizations around us, sometimes without our awareness.In this course, we will rigorously apply machine learning techniques to real-world data to solve real-world problems. We will briefly study the underlying principles of diverse machine learning approaches such as anomaly detection, ensemble learning, deep learning with a neural network, etc. The main focus will be applying tool libraries from the Python-based Anaconda and Java-based Weka data science platforms to datasets from online resources such as Kaggle, UCI KDD, open source repositories, etc. We will also use Jupyter notebooks to present and demonstrate several machine learning pipelines.
Achieving the full capability of AI requires a system perspective to effectively leverage algorithms, data, and computing power. Creating AI-enabled systems includes thoughtful consideration of an operational decomposition for AI solutions, engineering data for algorithm development, and deployment strategies. To realize the impact of AI technologies requires a systems perspective that goes beyond the algorithms. The objective of this course is to bring a system perspective to creating AI-enabled systems. The course will explore the full-lifecycle of creating AI-enabled systems starting with problem decomposition and addressing data, design, diagnostic, and deployment phases. The course will also cover ethics and bias in AI systems. The course includes a systems project that will encompass the full-lifecycle with interim milestones throughout the course. Homework assignments will be provided that in involves python programming.
Biomedical Engineering
An intermediate biomedical imaging course covering modern biomedical imaging instrumentation and techniques as applied to diagnostic radiology and other biomedical applications. It includes recent advances in various biomedical imaging modalities, multi-modality imaging and molecular imaging. The course is team taught by experts in the respective fields and provides a broad based knowledge of modern biomedical imaging to prepare students for graduate studies and research in biomedical imaging. Also, the course will offer tours and practical experience with modern biomedical imaging equipment in clinical and research settings. Co-listed with EN.520.434Recommended course background: EN.520.432 or EN.580.472
Prerequisite(s): Students may take EN.580.773 or EN.580.473 but not both.
Area: Engineering, Natural Sciences
This course is designed to give students a foundation in the basics of statistical and algorithmic approaches developed in computational biology/bioinformatics over the past 30 years, while emphasizing the need to extend these approaches to emerging problems in the field. Topics covered include probabilistic modeling applied to biological sequence analysis, supervised machine learning, interpretation of genetic variants, cancer genomics bioinformatic workflows and computational immuno-oncology. Attending the lab section "Annotate Your Genome" is required.
Prerequisite(s): EN.601.220
Area: Engineering, Natural Sciences
This course will introduce probabilistic modeling and information theory applied to biological sequence analysis, focusing on statistical models of protein families, alignment algorithms, and models of evolution. Topics will include probability theory, score matrices, hidden Markov models, maximum likelihood, expectation maximization and dynamic programming algorithms. Homework assignments will require programming in Python. Recommended Course Background: Math through linear algebra and differential equations, EN.580.221 or equivalent, EN.601.226 or equivalent.
The past few years have seen a dramatic increase in the performance of recognition systems thanks to the introduction of deep networks for representation learning. However, the mathematical reasons for this success remain elusive. For example, a key issue is that the training problem is nonconvex, hence optimization algorithms are not guaranteed to return a global minima. Another key issue is that while the size of deep networks is very large relative to the number of training examples, deep networks appear to generalize very well to unseen examples and new tasks. This course will overview recent work on the theory of deep learning that aims to understand the interplay between architecture design, regularization, generalization, and optimality properties of deep networks. Recommended background: machine learning (EN.601.475), deep learning (EN.520.438 or EN.601.482), graduate-level matrix analysis and linear algebra (EN.553.792) and graduate-level optimization (EN.553.762).
Cognitive Science
The course gives an introduction to computational models of the mammalian visual cortex. It covers topics in low-, mid-, and high-level vision. It briefly discusses the relevant evidence from anatomy, electrophysiology, imaging (e.g., fMRI), and psychophysics. It concentrates on mathematical modeling of these phenomena taking into account recent progress in probabilistic models of computer vision and developments in machine learning, such as deep networks.Required Background: Calculus I and experience in a programming language (Python preferred).
Prerequisite(s): AS.110.106 OR AS.110.108
Area: Quantitative and Mathematical Sciences
The course gives an introduction to computational models of the mammalian visual cortex. It covers topics in low-, mid-, and high-level vision. It briefly discusses the relevant evidence from anatomy, electrophysiology, imaging (e.g., fMRI), and psychophysics. It concentrates on mathematical modelling of these phenomena taking into account recent progress in probabilistic models of computer vision and developments in machine learning, such as deep networks.Also offered as AS.050.375.
This seminar is based on topics in computational vision with the option of attending additional subgroup meetings on specific topics.
Cybersecurity
This course surveys the broad fields of enterprise security and privacy, concentrating on the nature of enterprise security requirements by identifying threats to enterprise information technology (IT) systems, access control and open systems, and system and product evaluation criteria. Risk management and policy considerations are examined with respect to the technical nature of enterprise security as represented by government guidance and regulations to support information confidentiality, integrity and availability. The course develops the student’s ability to assess enterprise security risk and to formulate technical recommendations in the areas of hardware and software. Aspects of security-related topics to be discussed include network security, cryptography, IT technology issues, and database security. The course addresses evolving Internet, Intranet, and Extranet security issues that affect enterprise security. Additional topics include access control (hardware and software), communications security, and the proper use of system software (operating system and utilities). The course addresses the social and legal problems of individual privacy in an information processing environment, as well as the computer “crime” potential of such systems. The class examines several data encryption algorithms. Course Note(s): This course can be taken before or after 605.621 Foundations of Algorithms. It must be taken before other courses in the degree.
While most of the world is preoccupied with high-profile network-based computer intrusions, this online course examines the potential for computer crime and the protection mechanisms employed in conjunction with the embedded computers that can be found within non-networked products (e.g., vending machines, automotive onboard computers, etc.). This course provides a basic understanding of embedded computer systems: differences with respect to network-based computers, programmability, exploitation methods, and current intrusion protection techniques, along with material relating to computer hacking and vulnerability assessment. The course materials consist of a set of eight study modules and five casestudy experiments (to be completed at a rate of one per week) and are augmented by online discussion forums moderated by the instructor. This course also includes online discussion forums that support greater depth of understanding of the materials presented within the study modules.
Prerequisite(s): 605.202 Data Structures; 695.601 Foundations of Information Assurance, a basic understanding and working knowledge of computer systems, and access to Intel-based PC hosting a Microsoft Windows environment.
This course covers both the fundamentals and advanced topics in operating system (OS) security. Access control mechanisms (e.g., SACL/DACL), memory protections, and interprocess communications mechanisms will be studied. Students will learn the current state-of-the-art OS-level mechanisms and policies designed to help protect systems against sophisticated attacks. In addition, advanced persistent threats, including rootkits and malware, as well as various protection mechanisms designed to thwart these types of malicious activities, will be studied. Advanced kernel debugging techniques will be applied to understand the underlying protection mechanisms and analyze the malicious software. Students will learn both hardware and software mechanisms designed to protect the OS (e.g., NX/ASLR/SMEP/SMAP). The course will use virtual machines to study traditional OS environments on modern 64-bit systems (e.g., Windows, Linux, and macOS), as well as modern mobile operating systems (e.g., iOS and Android). Prerequisite(s): Familiarity with operating system concepts.
This course covers cybersecurity systems engineering principles of design. Students will learn the foundational and timeless principles of cybersecurity design and engineering. They will learn why theories of security come from theories of insecurity, the important role of failure and reliability in security, the fundamentals of cybersecurity risk assessment, the building blocks of cybersecurity, intrusion detection design, and advanced topics like cybersecurity situational understanding and command and control. The course develops the student’s ability to understand the nature and source of risk to a system, prioritize those risks, and then develop a security architecture that addresses those risks in a holistic manner, effectively employing the building blocks of cybersecurity systems— prevention, detection, reaction, and attack-tolerance. The student will learn to think like a cyber-attacker so that they can better design and operate cybersecurity systems. Students will attain the skill of systematically approaching cybersecurity from the top down and the bottom up and have confidence that their system designs will be effective at addressing the full spectrum of the cyber-attack space. The course also addresses how the cybersecurity attack and defense landscape will evolve so that the student is not simply ready to address today’s problems, but can quickly adapt and prepare for tomorrow’s. The course is relevant at any stage in a student’s curriculum: whether at the beginning to enable the student to understand the big picture before diving into the details, at the end as a capstone, or in the middle to integrate the skills learned to date.
Prerequisite(s): 695.601 Foundations of Information Assurance.
The age of cyber physical systems (CPS) has officially begun. Not long ago, these systems were separated into distinct domains, cyber and physical. Today, the rigid dichotomy between domains no longer exists. Cars have programmable interfaces, unmanned aerial vehicles (UAVs) roam the skies, and critical infrastructure and medical devices are now fully reliant on computer control. With the increased use of CPS and the parallel rise in cyber-attack capabilities, it is imperative that new methods for securing these systems be developed. This course will investigate key concepts behind CPS including control systems, protocol analysis, behavioral modeling, and intrusion detection system (IDS) development. The course will comprise theory, computation, and projects to better enhance student learning and engagement, beginning with the mathematics of continuous and digital control systems and then focusing on the complex world of CPS, where general overviews for the different domains (industrial control, transportation, medical devices, etc.) are complemented with detailed case studies (Siemens ICS & ArduPilot UAVs). Several advanced topics, including behavioral analysis and resilient CPS, will be introduced. Students will complete several projects, both exploiting security vulnerabilities and developing security solutions for UAVs and industrial controllers. Prerequisite(s): Knowledge of IP addresses and packets, matrix algebra, and Windows and Linux operating systems.
This course describes public key technology and related security management issues in the context of the Secure Cyberspace Grand Challenge of the National Academy of Engineering. Course materials explain Public Key Infrastructure (PKI) components and how the various components support e-business and strong security services. The course includes the basics of public key technology; the role of digital certificates; a case study that emphasizes the content and importance of certificate policy and certification practices; identification challenges and the current status of the National Strategy for Trusted Identities in Cyberspace; and essential aspects of the key management lifecycle processes that incorporate the most recent research papers of the National Institute of Standards and Technology. Students will examine PKI capabilities and digital signatures in the context of the business environment, including applicable laws and regulations. The course also presents the essential elements for PKI implementation, including planning, the state of standards, and interoperability challenges. The course also provides an opportunity for students to tailor the course to meet specific cybersecurity interests with regard to PKI and participate in discussions with their peers on contemporary cybersecurity topics.
This course examines issues associated with making web applications secure. The principal focus is on server-side security such as CGIsecurity, proper server configuration, and firewalls. The course also investigates the protection of connections between a client and server using current encryption protocols (e.g., SSL/TLS) as well discussing the related attacks on these protocols (e.g., Heartbleed, CRIME, etc.). The course also investigates keeping certain data private from the server system (e.g., via third-party transaction protocols like SET, or PCI DSS standard). Elementary Number Theory will be reviewed. Finally, the course explores client-side vulnerabilities associated with browsing the web, such as system penetration, information breach, identity theft, and denial-of-service attacks. Related topics such as malicious e-mails, web bugs, spyware,and software security are also discussed. Labs and various serverside demonstrations enable students to probe more deeply into security issues and to develop and test potential solutions. Basic knowledge of operating systems is recommended. Students will download and install a Virtual Machine to be used in the course. Prerequisite(s): 605.202 Data Structures
This course provides an introduction to the principles and practice of contemporary cryptology. It begins with a brief survey of classical cryptographic techniques that influenced the modern development of cryptology. The course then focuses on contemporary work: symmetric block ciphers and the Advanced Encryption Standard, public key cryptosystems, digital signatures, authentication protocols, cryptographic hash functions, and cryptographic protocols and their applications. Pertinent ideas from complexity theory and computational number theory, which provide the foundation for much of the contemporary work in cryptology, are introduced as needed throughout the course. Course Note(s): This course should be taken after the other two required foundation courses and before any other courses in the Analysis track.
Prerequisite(s): EN.695.601 AND EN.605.621OR EN.605.601[C] AND EN.605.611 AND EN.605.621
This course explores the use of intrusion detection systems (IDS) as part of an organization’s overall security posture. A variety of approaches, models, and algorithms along with the practical concerns of deploying IDS in an enterprise environment will be discussed. Topics include the history of IDS, anomaly and misuse detection for both host and network environments, and policy and legal issues surrounding the use of IDS. The use of ROC (receiver operating characteristic) curves to discuss false positives and missed detection tradeoffs as well as discussion of current research topics will provide a comprehensive understanding of when and how IDS can complement host and network security. TCPDump and Snort will be used in student assignments to collect and analyze potential attacks. Prerequisite(s): 605.202 Data Structures; 605.101 Introduction to Python or knowledge of Python.
This course exposes students to the world of computer hacking. The primary goal is to give students an understanding of how vulnerable systems can be attacked as a means to motivate how they might be better defended. The class takes a systems engineering view of hacking and emphasizes practical exposure via hands-on assignments. Students are expected to use a computer that will remain off all networks while they complete assignments.
Prerequisite(s): 695.601 Foundations of Information Assurance and one of 635.611 Principles of Network Engineering or 605.671 Principles of Data Communications Networks. Course Note(s): Homework assignments will include programming.
This course examines security topics in the context of the Java language with emphasis on security services such as confidentiality, integrity, authentication, access control, and nonrepudiation. Specific topics include mobile code, mechanisms for building “sandboxes” (e.g., class loaders, namespaces, bytecode verification, access controllers, protection domains, policy files), symmetric and asymmetric data encryption, hashing, digital certificates, signature and MAC generation/verification, code signing, key management, SSL, and object-level protection. Various supporting APIs are also considered, including the Java Cryptography Architecture (JCA) and Java Cryptography Extension (JCE). Security APIs for XML and web services, such as XML Signature and XML Encryption, Security Assertions Markup Language (SAML), and Extensible Access Control Markup Language (XACML), are also surveyed. The course includes multiple programming assignments and a project.
Prerequisite(s): 605.681 Principles of Enterprise Web Development or equivalent. Basic knowledge of XML. 695.601 Foundations of Information Assurance or 695.622 Web Security would be helpful but is not required.
Authentication technologies in cybersecurity play an important role in identification, authentication, authorization, and non-repudiation of an entity. The authentication process in cybersecurity, which is considered to be one of the weakest links in computer security today, takes many forms as new technologies such as cloud computing, mobile devices, biometrics, PKI, and wireless are implemented. Authentication is the security process that validates the claimed identity of an entity, relying on one or more characteristics bound to that entity. An entity can be, but is not limited to, software, firmware, physical devices, and humans. The course explores the underlying technology, the role of multi-factor authentication in cyber security, evaluation of authentication processes, and the practical issues of authentication. Several different categories and processes of authentication will be explored along with password cracking techniques, key logging, phishing, and man-in-the-middle attacks. Examples of authentication breaches and ethical hacking techniques will be explored to examine the current technologies and how they can be compromised. Case studies of authentication system implementation and their security breaches are presented. Federated authentication process over different network protocols, topologies, and solutions will be addressed. Related background is developed as needed, allowing students to gain a rich understanding of authentication techniques and the requirements for using them in a secure environment including systems, networks, and the Internet. Students will present a research project that reflects an understanding of key issues in authentication.
Prerequisite(s): 605.202 Data Structures; 695.601 Foundations of Information Assurance. 695.621 Public Key Infrastructure and Managing E-Security is recommended.
Autonomic systems leverage the growing advances in control, computer vision, and machine learning coupled with technological advances in sensing, computation, and communication. While this emerging highly connected, autonomous world is full of promise, it also introduces safety and security risks that are not present in legacy systems. This course focuses on the complexities inherent in autonomous systems and the multifaceted and multilayered approaches necessary to assure their secure and safe operation. As these systems become more pervasive, guaranteeing their safe operation even during unforeseen and unpredictable events becomes imperative. There are currently no real solutions to provide these runtime guarantees necessitating cutting edge research to provide state awareness, intelligence, control, safety, security, effective human-machine interaction, robust communication, and reliable computation and operation to these systems. This course follows a seminar-style format where students are expected to lead class discussions and write a publication-quality paper as part of a course project.
This course covers concepts and issues pertaining to network security and network security architecture and evolving virtualization and related cloud computing security architecture. Topics include mini-cases to develop a network security context. For example, we will assess the NIST (National Institute of Standards and Technology) unified information security framework. This framework is supported by information security standards and guidance, such as a risk management framework (RMF) and continuous monitoring (CM) process. Applied cryptography and information security—encryption algorithms, hash algorithms, message integrity checks, digital signatures, security assessment and authentication, authorization and accounting (AAA), security association, and security key management (generation, distribution, and renewal)—are discussed with consideration given to emerging cryptographic trends, such as the evolution and adoption of NSA’s (National Security Agency’s) Suite B cryptography. This course presents network and network security architecture viewpoints for selected security issues, including various security mechanisms, different layers of wired/wireless security protocols, different types of security attacks and threats and their countermeasures or mitigation, Next Generation Network (NGN) security architecture that supports the merging of wired and wireless communications, and Internet Protocol version 6 implementation and transition. The course concludes with more comprehensive cases that consider network security aspects of virtualization and cloud computing architecture.
Prerequisite(s): 605.202 Data Structures; 695.601 Foundations of Information Assurance and 605.671 Principles of Data Communications Networks or 635.611 Principles of Network Engineering.
This course provides students with an overview of analysis as it applies to information assurance. Analysis is a fundamental part of the information assurance process, and effective analysis informs policy, software development, network operations, and criminal investigations. To enable students to perform effective analysis, the focus of the course is on the analysis process and approach rather than on specific tools. Topics include the collection, use, and presentation of data from a variety of sources (e.g., raw network traffic data, traffic summary records, and log data collected from servers and firewalls). These data are used by a variety of analytical techniques, such as collection approach evaluation, population estimation, hypothesis testing, experiment construction and evaluation, and constructing evidence chains for forensic analysis. Students will construct and critique an analytical architecture, construct security experiments, and retroactively analyze events. The course will also cover selected non-technical ramifications of data collection and analysis, including anonymity, privacy, and legal constraints.
Prerequisite(s): 695.601 Foundations of Information Assurance. Familiarity with basic statistical analysis. 695.642 Intrusion Detection or 695.611 Embedded Computer SystemsVulnerabilities, Intrusions, and Protection Mechanisms is recommended.
Digital forensics focuses on the acquisition, identification, attribution, and analysis of digital evidence of an event occurring in a computer or network. This course provides a broader scientific understanding of the technologies and techniques used to perform digital forensics. In particular, various signature extraction techniques, detection, classification, and retrieval of forensically interesting patterns will be introduced. This will be complemented by studying fundamental concepts of data processing technologies like compression, watermarking, steganography, cryptography, and multiresolution analysis. Emerging standards along with issues driving the changing nature of this topic will be explored. Antiforensic techniques that are used to counter forensic analysis will also be covered. Students will be exposed to relevant theory, programming practice, case studies, and contemporary literature on the subject.
Prerequisite(s): 605.612 Operating Systems.
This course covers both the art and science of discovering software vulnerabilities. Beginning with the foundational techniques used to analyze both source and binary code, the course will examine current threats and discuss the actions needed to prevent attackers from taking advantage of both known and unknown vulnerabilities. The course will cover passive and active reverse engineering techniques in order to discover and categorize software vulnerabilities, create patches and workarounds to better secure the system, and describe security solutions that provide protection from an adversary attempting to exploit the vulnerabilities. Techniques covered include the use of static analysis, dynamic reverse engineering tools, and fault injection via fuzzing to better understand and improve the security of software.
Students will learn about the nature and purpose of cyber exercises and their role in training and assessing people, teams, technology, and procedures. During the course of the semester, students will design a cyber exercise that meets the specific needs of their organization. At the conclusion of the class, students will have a model template they can use to design, build, and execute their own exercise.
This course explores concepts and issues pertaining to information assurance architectures (IAA) and technologies, such as layered security architecture guidance and cases from the National Institute of Standards and Technology (NIST) and NIST Cybersecurity Center of Excellence (NCCoE); cryptographic commercial issues and evolving federal guidance; hypervisor and cloud computing security architecture; and IAA and technologies applications. Topics include critical infrastructure protection and Comprehensive National Cybersecurity Initiative (CNCI) Trusted Internet Connections (TIC) 2.0 multi-agency security information management (SIM) and selected security analytics issues. Commercial IAA examples of network security architecture and security analytics are also discussed for evolving enterprise mobility issues. The relationships of IAA and technologies with selected multi-tier architectures are discussed for applications such as enterprise risk management; security for virtualized environments; systems security engineering for services; and mobile device security. IAA multi-tier architecture issues are illustrated with cases, such as the NIST NCCoE use cases for Data Integrity: Recovering from Ransomware and Other Destructive Events; Access Rights Management for the Financial Services Sector; Situational Awareness for Electric Utilities; and Derived Personal Identity Verification (PIV) Credentials. Selected large-scale programs are discussed, such as enterprise risk management for the Federal Aviation Administration (FAA) Air Traffic Modernization process; and NIST Smart Grid Cybersecurity Strategy, Architecture, and HighLevel Requirements.
Prerequisite(s): 605.202 Data Structures; 695.601 Foundations of Information Assurance or equivalent, and 605.671 Principles of Data Communications Networks or 635.611 Principles of Network Engineering.
Data Science
This course will cover the core concepts and skills in the emerging field of data science. These include problem identification and communication, probability, statistical inference, visualization, extract/transform/load (ETL), exploratory data analysis (EDA), linear and logistic regression, model evaluation and various machine learning algorithms such as random forests, k-means clustering, and association rules. The course recognizes that although data science uses machine learning techniques, it is not synonymous with machine learning. The course emphasizes an understanding of both data (through the use of systems theory, probability, and simulation) and algorithms (through the use of synthetic and real data sets). The guiding principles throughout are communication and reproducibility. The course is geared towards giving students direct experience in solving the programming and analytical challenges associated with data science. Prerequisite(s): Programming experience in Python is recommended.
Prerequisite(s): Principles of Algorithms;
Electrical Computer Engineering
This course teaches the basics of switch-level digital CMOS VLSI design. This includes creating digital gates using MOS transistors as switches, laying out a design using CAD tools, and checking the design for conformance to the Scalable CMOS design rules.Recommended: EN.520.213.
Prerequisite(s): (AS.171.101 AND AS.171.102) OR (AS.171.101 AND AS.171.108) OR ( AS.171.102 AND AS.171.107) OR ( AS.171.107 AND AS.171.108)
Area: Engineering
This course introduces the student to the programming of microprocessors at the machine level. 68HC08, 8051, and eZ8 microcontrollers are programmed in assembly language for embedded control purposes. The architecture, instruction set, and simple input/output operations are covered for each family. Upon completion, students can use these flash-based chips as elements in other project courses. Recommended Course Background: EN.520.142 or equivalent.The lab is open 24/7 and students can still take the class if they are unable to meet during lab time.
Prerequisite(s): Students must have completed Lab Safety training prior to registering for this class. To access the tutorial, login to myLearning and enter 458083 in the Search box to locate the appropriate module.
Area: Engineering
This course will focus on the use of machine learning theory and algorithms to model, classify and retrieve information from different kinds of real world complex signals such as audio, speech, image and video.
Prerequisite(s): Students can only take EN.520.412 OR EN.520.612, not both.;AS.110.201 AND EN.553.310 AND (EN.520.435 OR EN.520.344)
Area: Engineering
An advanced laboratory course in the application of FPGA technology to information processing, using VHDL synthesis methods for hardware development. The student will use commercial CAD software for VHDL simulation and synthesis, and implement their systems in programmable XILINX 20,000 gate FPGA devices. The lab will consist of a series of digital projects demonstrating VHDL design and synthesis methodology, building up to final projects at least the size of an 8-bit RISC computer. Projects will encompass such things as system clocking, flip-flop registers, state-machine control, and arithmetic. The students will learn VHDL methods as they proceed through the lab projects, and prior experience with VHDL is not a prerequisite.
Prerequisite(s): Students must have completed Lab Safety training prior to registering for this class. To access the tutorial, login to myLearning and enter 458083 in the Search box to locate the appropriate module.
Area: Engineering, Quantitative and Mathematical Sciences
This course provides students with an introduction to the physics, instrumentation, and signal processing methods used in general radiography, X-ray computed tomography, ultrasound imaging, magnetic resonance imaging, and nuclear medicine. The primary focus is on the methods required to reconstruct images within each modality from a signals and systems perspective, with emphasis on the resolution, contrast, and signal-to-noise ratio of the resulting images. Students will additionally engage in hands-on activities to reconstruct medical images from raw data.
Prerequisite(s): EN.520.214 OR EN.580.222 OR (EN.580.243 AND EN.580.246)
Area: Engineering
This course covers the principles and algorithms used in the processing and analysis of medical images. Topics include, interpolation, registration, enhancement, feature extraction, classification, segmentation, quantification, shape analysis, motion estimation, and visualization. Analysis of both anatomical and functional images will be studied and images from the most common medical imaging modalities will be used. Projects and assignments will provide students experience working with actual medical imaging data.
Prerequisite(s): EN.550.310 OR EN.550.311 OR EN.560.348
Area: Engineering
An intermediate biomedical imaging course covering modern biomedical imaging instrumentation and techniques as applied to diagnostic radiology and other biomedical applications. It includes recent advances in various biomedical imaging modalities, multi- modality imaging and molecular imaging. The course is team taught by experts in the respective fields and provides a broad based knowledge of modern biomedical imaging to prepare students for graduate studies and research in biomedical imaging. Also, the course will offer tours and practical experience with modern biomedical imaging equipments in clinical and research settings. Co-listed with EN.580.473
Prerequisite(s): Students may not have taken EN.520.634
This course will address some basic scientific questions about systems that store or communicate information. Mathematical models will be developed for (1) the process of error-free data compression leading to the notion of entropy, (2) data (e.g. image) compression with slightly degraded reproduction leading to rate-distortion theory and (3) error-free communication of information over noisy channels leading to the notion of channel capacity. It will be shown how these quantitative measures of information have fundamental connections with statistical physics (thermodynamics), computer science (string complexity), economics (optimal portfolios), probability theory (large deviations), and statistics (Fisher information, hypothesis testing).
Prerequisite(s): EN.553.310 OR EN.553.420 OR EN.553.311;Students can earn credit for either EN.520.447 or EN.520.647, but not both.
Area: Engineering, Quantitative and Mathematical Sciences
An advanced laboratory course in which teams of students design, build, test and document application specific information processing microsystems. Semester long projects range from sensors/actuators, mixed signal electronics, embedded microcomputers, algorithms and robotics systems design. Demonstration and documentation of projects are important aspects of the evaluation process.Recommended: EN.600.333, EN.600.334, EN.520.214, EN.520.216, EN.520.349, EN.520.372, EN.520.490 or EN.520.491.
Prerequisite(s): Students must have completed Lab Safety training prior to registering for this class. To access the tutorial, login to myLearning and enter 458083 in the Search box to locate the appropriate module.;(EN.520.240 OR EN.520.340 OR EN.520.230 OR EN.520.213) AND AS.110.108 AND AS.110.109 AND ((171.101 AND 171.102) OR (171.101 AND 171.108) OR (171.102 AND 171.107) OR (171.107 AND 171.108)) AND EN.520.142.
This course covers the usage of common microcontroller peripherals. Interrupt handling, timer operations, serial communication, digital to analog and analog to digital conversions, and flash ROM programming are done on the 68HC08, 8051, and eZ8 microcontrollers. Upon completion, students can use these flash-based chips as elements in other project courses. Recommended Course Background: EN.520.349
Prerequisite(s): Students must have completed Lab Safety training prior to registering for this class. To access the tutorial, login to myLearning and enter 458083 in the Search box to locate the appropriate module.
Juniors and Seniors Only.
Prerequisite(s): Student may take EN.520.491 or EN.520.691, but not both.;AS.110.109 AND (AS.171.102 OR AS.171.104 OR AS.171.108) AND EN.520.142 AND EN.520.142 AND ( EN.520.230 OR ( EN.520.213 AND EN.520.345 OR EN.520.216 ) )
Area: Engineering
This course will focus on the use of machine learning theory and algorithms to model, classify and retrieve information from different kinds of real world complex signals such as audio, speech, image and video. Recommended Course Background: AS.110.201, EN.553.310, and EN.520.435.
Prerequisite(s): Students must have completed Lab Safety training prior to registering for this class. To access the tutorial, login to myLearning and enter 458083 in the Search box to locate the appropriate module.;Credit may only be earned for EN.520.412 or EN.520.612.
Area: Engineering
This biweekly seminar will cover a broad range of current research topics in human language technology, including automatic speech recognition, natural language processing and machine translation. The Tuesday seminars will feature distinguished invited speakers, while the Friday seminars will be given by participating students. A minimum of 75% attendance and active participation will be required to earn a passing grade. Grading will be S/U.
This biweekly seminar will cover a broad range of current research topics in human language technology, including automatic speech recognition, natural language processing and machine translation. The Tuesday seminars will feature distinguished invited speakers, while the Friday seminars will be given by participating students. A minimum of 75% attendance and active participation will be required to earn a passing grade. Cross-listed with Computer Science. Grading will be S/U.
Area: Engineering
Electrical and Computer Engineering
This is a hands-on course that integrates teaching of concepts in wireless LANs as well as offering students, in an integrated lab environment, the ability to conduct laboratory experiments and design projects that cover a broad spectrum of issues in wireless LANs. The course will describe the characteristics and operation of contemporary wireless network technologies such as the IEEE 802.11 and 802.11s wireless LANs and Bluetooth wireless PANs. Laboratory experiments and design projects include MANET routing protocols, infrastructure and MANET security, deploying hotspots, and intelligent wireless LANs. The course will also introduce tools and techniques to monitor, measure, and characterize the performance of wireless LANs as well as the use of network simulation tools to model and evaluate the performance of MANETs.
Prerequisite(s): EN.525.641 Computer and Data Communication Networks or EN.605.671 Principles of Data Communications Networks.
General Engineering
This course introduces fundamental programming concepts and techniques, and is intended for all who plan to develop computational artifacts or intelligently deploy computational tools in their studies and careers. Topics covered include the design and implementation of algorithms using variables, control structures, arrays, functions, files, testing, debugging, and structured program design. Elements of object-oriented programming. algorithmic efficiency and data visualization are also introduced. Students deploy programming to develop working solutions that address problems in engineering, science and other areas of contemporary interest that vary from section to section. Course homework involves significant programming. Attendance and participation in class sessions are expected.
Prerequisite(s): Students may not have earned credit in courses: EN.500.113 OR EN.500.114 OR EN.510.202 OR EN.530.112 OR EN.580.200 OR EN.601.107.
Area: Engineering
This on-line course provides students who have already achieved a basic understanding of programming and computational thinking in one programming language with an opportunity to apply these skills in another programming language. Students will be expected to complete projects to demonstrate proficiency in the new language. Satisfactory/unsatisfactory only.
Prerequisite(s): Not open to students who have completed EN.601.107, EN.600.107, or EN.500.112;Students must have completed: EN.500.113 OR EN.500.114 OR EN.510.202 OR EN.580.200 OR EN.530.112 OR EN.520.123 OR EN.601.220
Area: Engineering
This on-line course provides students who have already achieved a basic understanding of programming and computational thinking in one programming language with an opportunity to apply these skills in another programming language. Students will be expected to complete projects to demonstrate proficiency in the new language. Satisfactory/unsatisfactory only
Prerequisite(s): Not open to students who have completed EN.500.113 or EN.580.200;Students must have completed: EN.500.112 OR EN.500.114 OR EN.601.107 OR EN.510.202 OR EN.530.112 OR EN.520.123 OR EN.601.220
Area: Engineering
This on-line course provides students who have already achieved a basic understanding of programming and computational thinking in one programming language with an opportunity to apply these skills in another programming language. Students will be expected to complete projects to demonstrate proficiency in the new language. Satisfactory/unsatisfactory only.
Prerequisite(s): Not open to students who have completed EN.500.114 OR EN.580.200.;Students must have completed: EN.500.112 OR EN.500.113 OR EN.601.107 OR EN.510.202 OR EN.530.112 OR EN.520.123 OR EN.601.220
Area: Engineering
Seminar series in robotics. Topics include: Medical robotics, including computer-integrated surgical systems and image-guided intervention. Sensor based robotics, including computer vision and biomedical image analysis. Algorithmic robotics, robot control and machine learning. Autonomous robotics for monitoring, exploration and manipulation with applications in home, environmental (land, sea, space), and defense areas. Biorobotics and neuromechanics, including devices, algorithms and approaches to robotics inspired by principles in biomechanics and neuroscience. Human-machine systems, including haptic and visual feedback, human perception, cognition and decision making, and human-machine collaborative systems.Cross-listed Mechanical Engineering, Computer Science, Electrical and Computer Engineering, and Biomedical Engineering.
Information Security Institute
This course focuses on communication security in computer systems and networks. The course is intended to provide students with an introduction to the field of network security. The course covers network security services such as authentication and access control, integrity and confidentiality of data, firewalls and related technologies, Web security and privacy. Course work involves implementing various security techniques. A course project is required. Course Background: EN.601.220, EN.601.226, EN.601.418 or equivalent.
Prerequisite(s): Students may only earn credit for one of the following courses: EN.650.624 OR EN.601.444 OR EN.601.644
Area: Engineering
Cyber security affects every facet of industry and our government, and thus is now a threat to National Security. This course is designed to introduce students to the skills needed to defend computer network infrastructure by exposing them to the hands-on identification and exploitation of vulnerabilities in servers (i.e., Windows and Linux), wireless networks, websites, and cryptologic systems. These skills will be tested by having teams of students develop and participate in instructor lead capture-the-flag competitions. Also included are advanced topics such as shell coding, IDA Pro analysis, fuzzing, and writing or exploiting network-based applications or techniques such as web servers, spoofing, and denial of service.
Area: Engineering
Intrusion detection supports the on-line monitoring of computer system activities and the detection of attempts to compromise normal services. This course starts with an overview of intrusion detection tasks and activities. Detailed discussion introduces a traditional classification of intrusion detection models, applications in host-centered and distributed environments, and various intrusion detection techniques ranging from statistical analysis to biological computing. This course serves as a comprehensive introduction of recent research efforts in intrusion detection and the challenges facing modern intrusion detection systems. Students will also be able to pursue in-depth study of special topics of interest in course projects.
Area: Engineering, Natural Sciences
This course introduces students to the field of computer forensics and it will focus o n the various contemporary policy issues and applied technologies. Topics to be covered include: legal and regulatory issues, investigation techniques, data analysis approaches, and incident response procedures for Windows and UNIX systems. Homework in this course will relate to laboratory assignments and research exercises. Students should also expect that a group project will be integrated into this course.
Area: Engineering
Competent execution of security assessments on modern software systems requires extensive knowledge in numerous technical domains and comprehensive understanding of security risks. This course provides necessary background knowledge and examines relevant theories for software vulnerabilities and exploits in detail. Key topics include historical vulnerabilities, their corresponding exploits, and associated risk mitigations. Fundamental tools and techniques for performing security assessments (e.g., software reverse engineering, static analysis, and dynamic analysis) are covered extensively. The format of this course includes lectures and assignments where students learn how to develop exploits to well-known historical vulnerabilities in a controlled environment. Students will complete and demonstrate a project as part of the course.
Area: Engineering
Cloud computing promises significant cost savings via economies of scale that typically are not achievable by a single organization. This course examines cloud computing in detail and introduces the security concerns associated with cloud computing. Key topics include service models for cloud computing, virtualization, storage, management, and data processing. Fundamental security principles are introduced and applied to cloud computing environments. The format of this course includes lectures and hands-on assignments. Students will complete a project and present it as part of the course.
Area: Engineering, Natural Sciences
This course focuses on advanced security topics and research in computer networks. It builds on the basic overview of network security covered in previous security courses. Beyond the basics of developing security network communications and applications, this advanced course dives deeper into the theory and practice behind network attack, the growing reality of weaponized zero-day vulnerabilities, and the current state-of-the-art responses. Course work includes reviewing contemporary security research papers, hands-on experiments in defending/attacking networks, and writing analyses.
Prerequisite(s): EN.601.644 or permission of the instructor.
Area: Engineering
This course will analyze advanced topics and state of the art issues in the field of digital forensics. The course will be run in a research seminar format and students will be given both basic and applied research projects in such areas as: intrusion analysis, network forensics, memory forensics, mobile devices, and other emerging issues.
Information Systems Engineering
This course explores fundamental aspects of the e-Business (electronic business) phenomenon that is currently sweeping through the global economy, as well as design principles and technology used to build computer-based systems in order to support the notion of e-Business. E-Business (electronic business) is an umbrella term, an interdisciplinary topic encompassing both business and technology. This topic addresses a variety of business activities, business processes, and strategic business functions conducted over the Internet in order to service customers, to collaborate with business partners, and to maintain and sustain competitive advantage in the networking economy. The course introduces contemporary management philosophies as they have come to be used for the marketing, selling, and distribution of goods and services through the Internet and other electronic media. The course explores approaches of defining drivers and use cases of conducting electronic business. This course provides an overview of principles and analysis of different models of electronic business. It enables students to design effective e-Business models built on a foundation of business concepts, knowledge of the e-Business environment, and an understanding of the influence of the Internet on business stakeholders, including customers, suppliers, manufacturers, service makers, regulators, managers, and employees. In this course students undertake value analysis and learn to describe value propositions. Business architecture and software infrastructure used to engineer and build e-Business systems will be explained. The modern information technologies associated with the delivery of business capabilities over the Internet will be discussed. The course content will be reinforced by a variety of assignments.
Mechanical Engineering
This course seeks to introduce students to open-source software tools that are available today for building complex experimental and fieldable robotic systems. The course is grouped into sections, each of which building on the previous in increasing complexity and specificity: tools and frameworks supporting robotics research, robotics-specific software frameworks, integrating complete robotic systems, and culminates with an independent project of the student's own design using small mobile robots or other robots in the lab. Students will need to provide a computer (with at least a few GB of memory and a several tens of GB of disc space) running Ubuntu (https://www.ubuntu.com or one of its variants such as Xubuntu) and ROS (http://ros.org/). Students should have an understanding of intermediate programming in C/C++ (including data structures and object-oriented programming). Familiarity with Linux programming. Familiarity with software version control systems such as Git, and linear algebra. Students should see the course homepage https://dscl.lcsr.jhu.edu/home/courses/me530707-2019 for more information and to get started with the course. Required Course Prerequisite/Corequisite: EN.530.646 and EN.600.436. No audit option.
Physics Astronomy
The class will provide an overview of data science, with an introduction to basic statistical principles, databases, fundamentals of algorithms and data structures, followed by practical problems in data analytics. Recommend Course Background: Familiarity with principles of computing.
Area: Natural Sciences, Quantitative and Mathematical Sciences