General admission requirements for master’s degree candidates and others seeking graduate status are as follows: applicants must be in the last semester of undergraduate study or hold a bachelor’s degree from a regionally accredited college or university.
In addition, applicants for the Master of Science in Artificial Intelligence will likely have prior educational experience that includes an undergraduate or higher major in engineering or computer science. Applicants 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.
The applicant's prior education must include the following prerequisites:
- One semester/term of multivariate calculus;
- One semester/term of linear algebra;
- One semester/term of probability and statistics;
- One semester/term in a programming language such as Python;
- A second semester of programming experience
Applicants whose prior education does not include the prerequisites listed above may still enroll under provisional status, followed by full admission status once they have completed the missing prerequisites. Missing prerequisites may be completed with Johns Hopkins Engineering (all prerequisites are available) or at another regionally accredited institution. These prerequisite courses do not count toward the degree or certificate requirements. 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
In order to earn a Master of Science in Artificial Intelligence, the student must complete 30 approved credits within five years. The curriculum consists of 12 credits of core courses and 18 or more credits of electives from the Artificial Intelligence program. Nine (9) credits must be taken at the 700-level. 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. Only one C-range grade (C+ C, C-) can count toward the master’s degree. All course selections are subject to advisor approval.
Courses
Core Foundation courses
| Code | Title | Credits |
|---|---|---|
| A total of 4 core courses are required | ||
| EN.685.621 & EN.705.601 | Algorithms for Data Science and Applied Machine Learning (For an applied approach) | 6 |
| OR | ||
| EN.605.621 & EN.605.649 | Foundations of Algorithms and Introduction to Machine Learning (For a theoretical approach) | 6 |
| Followed by these 2 courses: | ||
| EN.605.645 | Artificial Intelligence | 3 |
| EN.705.603 | Creating AI-Enabled Systems | 3 |
| Code | Title | Credits |
|---|---|---|
| All students must take at least 6 of the following courses: | ||
| 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 |
| 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.605.635 | Cloud Computing | 3 |
| EN.605.646 | Natural Language Processing | 3 |
| EN.605.647 | Neural Networks | 3 |
| EN.605.662 | Data Visualization | 3 |
| EN.605.745 | Reasoning Under Uncertainty | 3 |
| EN.605.746 | Advanced Machine Learning | 3 |
| EN.605.747 | Evolutionary Computation | 3 |
| EN.625.638 | Neural Networks | 3 |
| EN.645.651 | Integrating Humans and Technology | 3 |
| EN.695.637 | Introduction to Assured AI and Autonomy | 3 |