To meet the growing demand for expertise in Artificial Intelligence, the Master of Science in Artificial Intelligence (MS-AI) will equip students with a strong foundation in AI. It will foster their proficiency in machine learning, deep learning, natural language processing, computer vision, and AI ethics. By providing a comprehensive and adaptable curriculum, we will prepare graduates for successful careers in the AI industry or research. Furthermore, we aspire to promote interdisciplinary collaboration by offering opportunities for students to specialize their degree to an area of interest in any discipline offered at WPI. The program balances technical expertise with its application in industry and/or government spaces via a capstone project or an MS thesis research experience. The program uses real-world experiential learning and research opportunities to ensure students are prepared for a rapidly evolving field and economic landscape.
Requirements for the Master of Science in Artificial Intelligence
Students must complete at least 30 credit hours of study in the M.S. program, which is equivalent to a minimum of ten 3-credit graduate courses. Students may select the MS thesis-option, which requires a 9- credit master’s thesis, or the project-based option, which requires a 3 credit capstone project course, known as the Graduate Qualifying Project (GQP). Each student should carefully weigh the pros and cons of these alternatives in consultation with their advisor prior to selecting an option, typically in the second year of study. The department will allow a student to change between the thesis or GQP options only once. All entering students must submit a Plan of Study, identifying the courses to be taken. The Plan of Study must be approved by the student’s advisor and the MS-AI Graduate Committee and must include the minimum requirements listed below. These M.S. degree requirements have been designed to provide a comprehensive yet flexible program to students who are pursuing an M.S. degree exclusively and also students who are pursuing a combined B.S./M.S. degree.
Core Courses:
MS-AI students must complete a five-course core by taking one course each in the five core MS-AI bins in AI, ethics & AI, machine learning, knowledge representation & reasoning, and interaction & action. Students must earn at least 15 core course credits. Students may choose to take additional core courses, beyond the five required core courses, from below bins:
For the capstone project, the MS-AI student can select one of the three capstone courses based on their primary interest and with approval of their MS-AI advisor and the instructor of the course. The MS-AI student must select a capstone project focused on Artificial Intelligence. The capstone project must be approved by a faculty member affiliated with the Artificial Intelligence Program. The capstone project is most commonly done in teams, and will give students an opportunity to apply Artificial Intelligence skills to a real-world problem. It will typically be carried out in cooperation with a sponsor or an industrial partner. With permission of the instructor, a capstone course can be taken a second time for a total of 6 credits.
The MS thesis in the Artificial Intelligence Program consists of a research or development project worth a minimum of 9 graduate credit hours. Students interested in research, and in particular those who consider pursuing a Ph.D. degree in a related area, are encouraged to select the M.S. thesis option. The student can sign up for MS thesis credits such as CS599, DS599, or RBE599, as long as a faculty affiliated with the MS-AI program serves as thesis advisor and the thesis topic relates to AI. Students must submit a thesis proposal for approval by the program by the end of the semester in which a student has registered for a third thesis credit and by the advisor. Proposals will be considered only at regularly scheduled program meetings. Students funded by a teaching assistantship, research assistantship or fellowship are expected to pursue the thesis option. The student then must satisfactorily complete a written thesis and present the results to the AI faculty in a public presentation.
Elective Courses
MS-AI students may choose to take additional elective courses or other AI-related courses to reach the 30-credit requirement:
- Other AI-related Courses: With permission from their academic advisor, students may take any number of AI-related special topics courses such as CS525/DS595/RBE595, Independent Study (ISG) and Directed Research (CS598/DS597/RBE596) as long as related to Artificial Intelligence and offered by faculty with a core or a collaborative appointment in the MS-AI program towards the degree.
- Electives: MS-AI students may choose to take up to at most six (6) graduate credits in courses that are not part of the MS-AI core bins in any discipline and count them towards the M.S. degree in Artificial Intelligence. If the academic unit offering the course has restrictions associated with the course, those restrictions must be followed.
- Specialization: Students may gain a specialization “AI&X” by ensuring 6 elective credits in the chosen discipline are selected from thematically-related courses in that area that are approved by the student’s Graduate Advisor. All requirements by the respective unit offering this course must be followed. These areas of specialization include, but are not limited to, the following:
- AI & Business: ML for Business, Project Management, Supply-Chain Optimization.
- AI & Engineered Systems: Digital Signal Processing, Medical Signal Analysis, Foundations of Robotics, Sensor Eng.
- AI & Foundations: Mathematical Optimization, Multi-variate Data Analysis, Advanced Statistics.
- AI & Game Development: Serious and Applied Games, Design of Interactive Experiences. 20
- AI & Global Development: Sustainability, Climate Change, Social Justice, Global Health
- AI & Health: BioInformatics, Health Sciences, Neuroscience, Biology.
- AI & Human Experiences: Human-Computer Interaction, Tangible & Embodied Interaction, Human-Robot Interaction, Visualization.
- AI & Learning Sciences: Foundations of Learning Sciences. Learning Environments in Education.
- AI & Material Sciences: Smart Materials, Nanomaterials, Manufacturing Processes
- AI & Neuroscience: Computational Neuroscience, Brain-Computer Interaction, Advanced Psychophysiology.
- AI & Robotics: Robot Dynamics, Biomedical Robotics, Soft Robotics.
- AI & Security: Software Security Design and Analysis, Machine Learning in Cybersecurity, Cryptography.
- AI & Software Systems: Adv. Software Eng., Algorithms, Mobile & Ubiquitous Computing, Distributed Systems.
- Specialization: Students may gain a specialization “AI&X” by ensuring 6 elective credits in the chosen discipline are selected from thematically-related courses in that area that are approved by the student’s Graduate Advisor. All requirements by the respective unit offering this course must be followed. These areas of specialization include, but are not limited to, the following:
Note 1: Less than 50% of the credits in the MS in Artificial Intelligence can be taken from the Business School, that is, a maximum of 14 credits of a 30-credit program. For 3-credit courses, a maximum of 4 courses may be taken from the Business School (any course with a prefix of ACC, BUS, ETR, FIN, MIS, MKT, OBC, or OIE).
Note 2: A single course cannot be used to meet two or more requirements of the MS-AI degree. For instance, if a course is used to meet one particular bin requirement, it cannot also be used to meet a second bin requirement, nor can it be counted towards fulfilling a thematically-related specialization.