The Master of Science (MS) in Artificial Intelligence (AI) Engineering is intended for students interested in developing the core engineering skills that will allow them to build and operate sophisticated AI systems in support of a broad range of applications. This requires deep knowledge of the algorithms on which those systems are based and sophisticated computational skills to implement those algorithms and select and operate the proper computing infrastructure. This can be either a pure course-option program, or it can incorporate a research experience via a project or a thesis. If a student chooses a degree option that incorporates a research experience, this MS degree may provide a solid stepping stone to future doctoral studies. All students in the MS in AI Engineering program must have previously completed (as documented by their undergraduate transcript), successfully test to place out of, or complete at the start of their program the following courses: CSE 1301 Introduction to Computer Science and CSE 2407 Data Structures and Algorithms (or equivalent courses offered at other institutions).
General Degree Requirements
The master's degree requires a minimum of 30 credit units, with 18 credit units of core requirements and 12 credit units of electives. A student may only complete at most 12 credit units at the 4000 level toward these requirements. Per School of Engineering guidelines, students must maintain a grade point average of at least 2.70. In addition, the required core courses must be completed with no more than one grade below a B, and electives must be completed with no more than one grade below a B–. Up to 6 graduate credit units may be transferred with the approval of the department, in alignment with policies from the McKelvey School Graduate Studies Committee and found in the McKelvey Bulletin.
The degree has three completion routes that may appeal to different students' interests: coursework only, project option, or thesis option. Students pursuing the coursework-only route will complete 30 units of traditional coursework via a combination of required core courses and elective courses. Students pursuing the project option will complete 24 to 27 units of traditional coursework and will enroll in 3 to 6 units of CSE 7998 Master's Capstone to complete a project. Students choosing to complete a 6-unit project must complete the work across two semesters. Students pursuing the thesis option will complete 24 units of traditional coursework and 6 units of CSE 7998 Master's Capstone over two semesters. For both a thesis and a project, students will orally defend their work and produce a written document. All project and thesis work will be graded as Pass/No Pass, and the units will not apply toward the student's degree requirements without a successful defense.
Required Core Courses
Students must complete 18 units total, with 3 units required from each area below:
1. Foundations of AI (3 units)
Course List
| Code |
Title |
Units |
| CSE 4102 | Introduction to Artificial Intelligence | 3 |
2. Machine Learning Foundations (Choose one)
Course List
| Code |
Title |
Units |
| CSE 4107 | Introduction to Machine Learning | 3 |
| CSE 5107 | Machine Learning | 3 |
| ESE 4170 | Introduction to Machine Learning and Pattern Classification | 3 |
| SDS 4430 | Statistical Learning | 3 |
3. Natural Language Processing (Choose one)
Course List
| Code |
Title |
Units |
| CSE 4027 | Introduction to Natural Language Processing | 3 |
| CSE 4061 | Text Mining | 3 |
| CSE 5270 | Natural Language Processing | 3 |
| CSE 5610 | Large Language Models | 3 |
4. Computer Vision (Choose one)
Course List
| Code |
Title |
Units |
| BME 5700 | Mathematics of Imaging Science | 3 |
| CSE 5509 | Computer Vision | 3 |
| ESE 5932 | Computational Methods for Imaging Science | 3 |
5. Human-Centered Computing (Choose one)
Course List
| Code |
Title |
Units |
| CSE 4507 | Introduction to Visualization | 3 |
| CSE 5108 | Human-In-The-Loop Computation | 3 |
| CSE 5506 | Human-Computer Interaction Methods | 3 |
| CSE 5507 | Advanced Visualization | 3 |
6. Computational Methods (Choose one)
Course List
| Code |
Title |
Units |
| CSE 4059 | Applied Parallel Programming: Gpus for High-Performance Computing On Manycore Architectures | 3 |
| CSE 4207 | Cloud Computing With Big Data Applications | 3 |
| CSE 5114 | Data Manipulation and Management at Scale | 3 |
Electives
At least 6 credit units of depth electives and 6 credit units of general elective coursework must be completed. These courses can be chosen from any of the following categories, for a total of at least 12 credit units (exclusive of any courses taken to fulfill the Core Requirements).
Depth Electives (6 units)
Students must choose at least two courses from the following list:
General Electives (6 units)
Students must choose two courses from any of the subareas below (exclusive of any courses taken to fulfill Core Requirements or Depth Electives):
1. Foundational and Applied AI
2. Theory
Course List
| Code |
Title |
Units |
| CSE 4470 | Introduction to Formal Languages and Automata | 3 |
| CSE 5401 | Advanced Algorithms | 3 |
| CSE 5404 | Special Topics in Computer Science Theory | 3 |
| CSE 5801 | Approximation Algorithms | 3 |
| CSE 5802 | Complexity Theory | 3 |
3. Security and Privacy
Course List
| Code |
Title |
Units |
| CSE 4103 | Web Privacy and Security | 3 |
| CSE 4303 | Introduction to Computer Security | 3 |
| CSE 4402 | Introduction to Cryptography | 3 |
| CSE 5271 | Data-Driven Privacy and Security | 3 |
| CSE 5619 | Recent Advances in Computer Security and Privacy | 3 |
4. Large-Scale Computing
Course List
| Code |
Title |
Units |
| CSE 4059 | Applied Parallel Programming: Gpus for High-Performance Computing On Manycore Architectures | 3 |
| CSE 4207 | Cloud Computing With Big Data Applications | 3 |
| CSE 5114 | Data Manipulation and Management at Scale | 3 |
| CSE 5606 | High Performance Computer Systems | 3 |
5. Robotics and Autonomous Systems
Course List
| Code |
Title |
Units |
| CSE 5370 | Trustworthy Autonomy | 3 |
| CSE 5500 | Mobile Robotics | 3 |
| ESE 4450 | Sensing, Planning, and Control in Robotics | 3 |
| ESE 4460 | Robotics: Dynamics and Control | 3 |
| ESE 4481 | Autonomous Aerial Vehicle Control Laboratory | 3 |
| ESE 5430 | Control Systems Design By State Space Methods | 3 |
| ESE 5470 | Robust and Adaptive Control | 3 |
| ESE 5510 | Linear Dynamic Systems I | 3 |
| ESE 5530 | Nonlinear Dynamic Systems | 3 |
6. Data Science
7. Imaging Science
Course List
| Code |
Title |
Units |
| CSE 5805 | Sparse Modeling for Imaging and Vision | 3 |
| CSE 5806 | Analysis of Imaging Data | 3 |
| ESE 5820 | Fundamentals and Applications of Modern Optical Imaging | 3 |
| | 3 |
| ESE 5932 | Computational Methods for Imaging Science | 3 |
| ESE 5933 | Theoretical Imaging Science | 3 |
8. Applied AI
Course List
| Code |
Title |
Units |
| CSE 5205 | Computation in Economics and Social Choice | 3 |
| CSE 5310 | AI for Health | 3 |
| CSE 5804 | Algorithms for Biosequence Comparison | 3 |
| CSE 5807 | Algorithms for Computational Biology | 3 |
9. Theory and Optimization
Course List
| Code |
Title |
Units |
| CSE 5313 | Coding and Information Theory for Data Science | 3 |
| CSE 5403 | Algorithms for Nonlinear Optimization | 3 |
| ESE 4150 | Optimization | 3 |
| ESE 5130 | Large-Scale Optimization for Data Science | 3 |
| SDS 5800 | Topics in Statistics | 3 |
10. Human-Centered Computing (any course not taken to satisfy the Core requirement)
Course List
| Code |
Title |
Units |
| CSE 4507 | Introduction to Visualization | 3 |
| CSE 5108 | Human-In-The-Loop Computation | 3 |
| CSE 5506 | Human-Computer Interaction Methods | 3 |
| CSE 5507 | Advanced Visualization | 3 |
11. MS Project or Thesis Options
CSE 7998 Master's Capstone: AI-related project or thesis
- Project Option: Students will enroll in at least one semester of CSE 7998 Master's Capstone (maximum 3 units per semester; maximum 6 units total) to complete a project that achieves a clear end result and/or product. The student will be expected to describe the results and products of the work, to articulate what was learned in an oral defense before a small committee of faculty, and to complete a short write-up about the experience.
- Thesis Option: Students will enroll in 6 units of CSE 7998 Master's Capstone (3 units per semester) to complete a master's thesis that produces an original research contribution. Students will orally defend the research and produce a 25- to 30-page document that describes the work itself, its significance, and the related literature in detail.
Additional Notes on the Thesis/Project Option
Each semester, CSE 7998 Master's Capstone will be graded as Pass/No Pass. A passing grade and completion of the progress report are required in the first semester of a 6-unit project or thesis to continue the work in the final semester. As a master's project or thesis is considered to have unique content each semester, CSE 7998 Master's Capstone is not a retakeable course, and a grade from a previous semester cannot be replaced. Students should refer to the department's Master's Program Handbook for additional details and guidance regarding projects and theses.
The Progress Report
Near the end of the first semester of a two-semester thesis or project, the student will submit a written progress report to the committee. The progress report will be used by the committee to evaluate student progress and to provide an opportunity to give feedback to the student. (Progress reports are required for two-semester theses and projects only.)