Admission Requirements
Applicants (degree-seeking and special students) must meet the general requirements for admission to graduate study. In addition, applicants for the Master of Science in Artificial Intelligence will likely have prior educational experience that includes an undergraduate or graduate degree in engineering or computer science. The applicant's prior education must include the following courses:
The applicant's prior education must include the following courses:
- Three semesters or five quarters of calculus, which includes multivariate calculus
- One semester/term of linear algebra
- One semester/term of probability and statistics
- Two semesters/terms of Python (which can include non-credit coursework such as Coursera or edX, etc. and EN.605.256 - Modern Software Concepts in Python or equivalent.)
Applicants whose prior education does not include the courses listed above may still enroll under provisional status, followed by full admission status once they have completed the missing courses. Missing courses may be completed with Johns Hopkins Engineering (all courses are available) or at another regionally accredited institution. Admitted students typically have earned a grade point average of at least 3.0 on a 4.0 scale (B or above) in the latter half of their undergraduate studies. Official transcripts from all college studies must be submitted. When reviewing an application, the candidate’s academic and professional background will be considered.
If you are an international applicant, you may have additional admission requirements.
Program Requirements
To earn a Master of Science in Artificial Intelligence, the student must complete ten graduate-level courses (30 credits) within five years. The curriculum consists of two core courses (6 credits), three required courses (9 credits), and five electives (15 credits). A minimum of three electives must be from the AI elective course list below and a maximum of two electives (6 credits) may be selected from any EP program. At least three courses (9 credits) must be 700-level.
Focus areas are provided as a guide for students to select courses. Students are not required to take courses from a specific focus area. The focus areas offered represent related groups of courses that are relevant for students with interests in the selected areas. The focus areas are only applicable to students seeking a master’s degree. They do not appear as official designations on a student’s transcript or diploma.
Only one C-range grade (C+ C, C-) can count toward the master’s degree. All course selections outside of the Artificial Intelligence program requirements are subject to advisor approval. Non-degree students in Artificial Intelligence should consult with their advisor to determine which courses must be successfully completed before 600- or 700-level courses may be taken.
Provisional Courses
Code | Title | Credits |
---|---|---|
Undergraduate-level courses offered to complete provisional requirements. 1,2 | Credits | |
EN.625.108 & EN.625.109 | Calculus I and Calculus II | 4 |
or EN.605.156 | Calculus for Engineers | |
EN.625.250 | Multivariable Calculus and Complex Analysis | 3 |
EN.625.240 | Introduction to Probability and Statistics | 3 |
EN.625.252 | Linear Algebra and Its Applications | 3 |
EN.605.206 | Introduction to Programming Using Python | 3 |
EN.605.256 | Modern Software Concepts in Python | 3 |
- 1
Applicants whose prior education does not include the courses listed under Admission Requirements may enroll under provisional status, followed by full admission once they have completed the missing provisional courses. All provisional courses are available at Johns Hopkins Engineering. These courses do not count toward the degree or certificate requirements.
- 2
Students who require EN.625.108 - Calculus I and EN.625.109 - Calculus II may elect to take the one semester course EN.605.156 - Calculus for Engineers OR AS.110.108 -Calculus I (Physical Sciences & Engineering) & AS.110.109 -Calculus II (Physical Sciences & Engineering).
Core and Required Courses
Code | Title | Credits |
---|---|---|
2 Core Courses 1 | Credits | |
EN.705.623 | AI Algorithm Design and Analysis 2 | 3 |
EN.705.603 | Creating AI-Enabled Systems | 3 |
3 Required Courses | ||
EN.705.601 | Applied Machine Learning | 3 |
EN.705.605 | Introduction to Generative AI | 3 |
or EN.705.608 | Applied Generative AI | |
EN.605.645 | Artificial Intelligence | 3 |
- 1
One or more core courses can be waived by the student’s advisor if a student has received an A or B in equivalent graduate courses. In this case, the student may replace the waived core courses with the same number of other graduate Artificial Intelligence courses and may take these courses after all remaining core course requirements have been satisfied.
- 2
EN.705.623 requirement may be satisfied with EN.685.621 Algorithms for Data Science.
Electives
Students must select a minimum of three electives from the AI elective course list below. A maximum of two electives (6 credits) may be chosen from any EP program. At least three courses (9 credits) must be 700-level.
Code | Title | Credits |
---|---|---|
Take at least 3 of the following courses (9 credits) | Credits | |
EN.705.604 | Production AI – Engineered AI Solutions | 3 |
EN.705.606 | Product Management for AI | 3 |
EN.705.612 | Values and Ethics in Artificial Intelligence | 3 |
EN.705.613 | Responsible AI | 3 |
EN.705.615 | Artificial Intelligence for Leaders | 3 |
EN.705.617 | Artificial Intelligence in Healthcare | 3 |
EN.705.618 | Neuromarketing AI | 3 |
EN.705.621 | Introduction to Algorithms | 3 |
EN.705.640 | Cognitive and Behavioral Foundations for Artificial Intelligence | 3 |
EN.705.641 | Natural Language Processing: Self-Supervised Models | 3 |
EN.705.643 | Deep Learning Developments with PyTorch | 3 |
EN.705.651 | Large Language Models: Theory and Practice | 3 |
EN.705.741 | Reinforcement Learning | 3 |
EN.705.742 | Advanced Applied Machine Learning | 3 |
EN.705.743 | ChatGPT from Scratch: Building and Training Large Language Models | 3 |
EN.705.744 | Deep Learning Using Transformers | 3 |
EN.605.613 | Introduction to Robotics | 3 |
EN.605.617 | Introduction to GPU Programming | 3 |
EN.605.624 | Logic: Systems, Semantics, and Models | 3 |
EN.605.633 | Social Media Analytics | 3 |
EN.605.634 | Crowdsourcing and Human Computation | 3 |
EN.605.635 | Cloud Computing | 3 |
EN.605.646 | Natural Language Processing | 3 |
EN.605.647 | Neural Networks | 3 |
EN.605.649 | Principles and Methods in Machine Learning | 3 |
EN.605.662 | Data Visualization | 3 |
EN.605.716 | Modeling and Simulation of Complex Systems | 3 |
EN.605.724 | Applied Game Theory | 3 |
EN.605.742 | Deep Neural Networks | 3 |
EN.605.743 | Advanced Artificial Intelligence | 3 |
EN.605.745 | Reasoning Under Uncertainty | 3 |
EN.605.746 | Advanced Machine Learning | 3 |
EN.605.747 | Evolutionary and Swarm Intelligence | 3 |
EN.695.637 | Introduction to Assured AI and Autonomy | 3 |
EN.695.715 | Assured Autonomy | 3 |
EN.635.603 | AI/ML Ops | 3 |
EN.645.651 | Integrating Humans and Technology | 3 |
EN.525.661 | UAV Systems and Control | 3 |
EN.525.670 | Machine Learning for Signal Processing | 3 |
EN.525.724 | Introduction to Pattern Recognition | 3 |
EN.525.733 | Deep Learning for Computer Vision | 3 |
EN.525.770 | Intelligent Algorithms | 3 |
EN.525.786 | Human Robotics Interaction | 3 |
Independent Study
Code | Title | Credits |
---|---|---|
EN.705.801 | Independent Study in Artificial Intelligence I | 3 |
EN.705.802 | Independent Study in Artificial Intelligence II | 3 |
Focus Areas
Assured Autonomy
Decision and Leadership
Deep Learning
Large Language Models (LLMs)
Robotics
Courses by Focus Area
The focus areas offered represent related groups of courses that are relevant for students with interests in the selected areas. Focus areas are presented as an aid to students in planning their course selections and are only applicable to students seeking a master’s degree. They do not appear as official designations on a student’s transcript or diploma.
Assured Autonomy
Code | Title | Credits |
---|---|---|
EN.605.636 | Autonomic Computing | 3 |
EN.605.745 | Reasoning Under Uncertainty | 3 |
EN.695.631 | AI for Cybersecurity | 3 |
EN.695.634 | Intelligent Vehicles: Cybersecurity for Connected and Autonomous Vehicles | 3 |
EN.695.637 | Introduction to Assured AI and Autonomy | 3 |
EN.695.715 | Assured Autonomy | 3 |
EN.695.737 | AI for Assured Autonomy | 3 |
EN.695.738 | Generative AI for Cybersecurity | 3 |
Decision and Leadership
Code | Title | Credits |
---|---|---|
EN.705.604 | Production AI – Engineered AI Solutions | 3 |
EN.705.606 | Product Management for AI | 3 |
EN.705.612 | Values and Ethics in Artificial Intelligence | 3 |
EN.705.613 | Responsible AI | 3 |
EN.705.615 | Artificial Intelligence for Leaders | 3 |
EN.705.617 | Artificial Intelligence in Healthcare | 3 |
EN.705.618 | Neuromarketing AI | 3 |
EN.705.640 | Cognitive and Behavioral Foundations for Artificial Intelligence | 3 |
EN.605.633 | Social Media Analytics | 3 |
EN.605.634 | Crowdsourcing and Human Computation | 3 |
EN.605.662 | Data Visualization | 3 |
EN.605.745 | Reasoning Under Uncertainty | 3 |
EN.635.603 | AI/ML Ops | 3 |
EN.635.622 | Intelligent Decision Engineering | 3 |
EN.635.627 | Intelligent Decision Support Systems | 3 |
EN.635.674 | AI for Entrepreneurs | 3 |
EN.635.775 | Cyber Operations, Risk, and Compliance | 3 |
EN.635.782 | Ethics in Intelligent Systems | 3 |
EN.645.651 | Integrating Humans and Technology | 3 |
Deep Learning
Code | Title | Credits |
---|---|---|
EN.705.621 | Introduction to Algorithms | 3 |
EN.705.643 | Deep Learning Developments with PyTorch | 3 |
EN.705.741 | Reinforcement Learning | 3 |
EN.705.742 | Advanced Applied Machine Learning | 3 |
EN.705.744 | Deep Learning Using Transformers | 3 |
EN.605.617 | Introduction to GPU Programming | 3 |
EN.605.647 | Neural Networks | 3 |
EN.605.649 | Principles and Methods in Machine Learning | 3 |
EN.605.740 | Machine Learning: Deep Learning | 3 |
EN.605.742 | Deep Neural Networks | 3 |
EN.525.670 | Machine Learning for Signal Processing | 3 |
EN.525.733 | Deep Learning for Computer Vision | 3 |
Large Language Models (LLMs)
Code | Title | Credits |
---|---|---|
EN.705.641 | Natural Language Processing: Self-Supervised Models | 3 |
EN.705.651 | Large Language Models: Theory and Practice | 3 |
EN.705.743 | ChatGPT from Scratch: Building and Training Large Language Models | 3 |
EN.705.744 | Deep Learning Using Transformers | 3 |
EN.605.617 | Introduction to GPU Programming | 3 |
EN.605.624 | Logic: Systems, Semantics, and Models | 3 |
EN.605.633 | Social Media Analytics | 3 |
EN.605.635 | Cloud Computing | 3 |
EN.605.646 | Natural Language Processing | 3 |
EN.605.647 | Neural Networks | 3 |
EN.605.716 | Modeling and Simulation of Complex Systems | 3 |
EN.605.724 | Applied Game Theory | 3 |
EN.605.742 | Deep Neural Networks | 3 |
EN.695.738 | Generative AI for Cybersecurity | 3 |
Robotics
Code | Title | Credits |
---|---|---|
EN.605.613 | Introduction to Robotics | 3 |
EN.605.649 | Principles and Methods in Machine Learning | 3 |
EN.605.743 | Advanced Artificial Intelligence | 3 |
EN.605.747 | Evolutionary and Swarm Intelligence | 3 |
EN.665.645 | Artificial Intelligence for Robotics | 3 |
EN.665.681 | Application of Sensing Systems | 3 |
EN.525.661 | UAV Systems and Control | 3 |
EN.525.724 | Introduction to Pattern Recognition | 3 |
EN.525.770 | Intelligent Algorithms | 3 |
EN.525.786 | Human Robotics Interaction | 3 |