Artificial Intelligence, Master of Science | Johns Hopkins University Academic Catalogue

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:

Three semesters or five quarters of calculus, which includes multivariate calculus or equivalent
One semester/term of linear algebra and one semester/term of probability and statistics or equivalent
Two semesters/terms of Python, Java, C++ (Python preferred). This must an intermediate-level course such as EN.605.256 Modern Software Concepts in Python or equivalent. Non-credit coursework may be considered for the first semester/term requirement.

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.

Provisional Courses

Course List
Code	Title	Credits
Undergraduate-level courses offered to complete provisional requirements. ¹		Credits
EN.625.108 & EN.625.109	Calculus I and Calculus II	4
or EN.605.156	Calculus for Engineers
EN.625.240 & EN.625.250 & EN.625.252	Introduction to Probability and Statistics and Multivariable Calculus and Complex Analysis and Linear Algebra and Its Applications	3
or EN.605.277	Applied Mathematical Methods for Engineers
EN.605.206	Introduction to Programming Using Python	3
EN.605.256	Modern Software Concepts in Python	3

¹: 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.

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 four core courses (12 credits) and six electives (18 credits). At least four of the six electives must be from the AI elective course list below and three of those must be at the 700-level. A maximum of two electives (6 credits) may be selected from any EP program, subject to advisor approval. Transfer courses will be considered electives. Transfer courses must meet all general Engineering for Professionals requirements for transfer, must be directly applicable to Artificial Intelligence, and will be considered on a case-by-case basis.

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.

Core Courses

Course List
Code	Title	Credits
4 Core Courses ¹		Credits
EN.705.623	AI Algorithm Design and Analysis ²	3
EN.705.603	Creating AI-Enabled Systems	3
EN.705.601	Applied Machine Learning	3
EN.705.605	Introduction to Generative AI	3
or EN.705.608	Applied Generative AI

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 six elective courses. A minimum of four electives must be from the AI elective course list below, and at least three of those courses (9 credits) must be 700-level. A maximum of two electives (6 credits) may be chosen from any EP program, subject to advisor approval.

Course List
Code	Title	Credits
Take at least 4 of the following courses (12 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.705.625	Introduction to Agentic AI	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.705.771	Introduction to Mechanistic Interpretability	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.605.635	Cloud Computing	3
EN.605.645	Artificial Intelligence	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.695.738	Generative AI for Cybersecurity	3
EN.695.739	Generative AI and Synthetic Threats	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

Course List
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.