Integrate AI-powered discoveries within domains like engineering, medicine, education, robotics, business, and governance. Examine AI's impact on communities, people, and the planet. Create the tools that will power the world forward. Gain hands-on experience through projects, co-ops, and internships.
Combine expertise in AI development with the practical skills, ethical considerations, and social understanding of the technology's impact.
Become an Expert
The Master's of Science in Artificial Intelligence (MSAI) offers two tracks designed to help students become experts within the field. Coursework balances the theoretical underpinnings of AI with the practical skills, ethics, and societal impacts of the technology on our world today. The program includes a core curriculum and department-specific tracks.
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The core curriculum covers advanced concepts in artificial intelligence, machine learning, ethics, and mathematics. All MS in Artificial Intelligence students must complete the following four courses:
Artificial Intelligence
- CS 131 Artificial Intelligence
Machine Learning
- CS 135 Introduction to Machine Learning and Data Mining, or
- EE 143/CS 144 Interative Methods in Machine Learning
Ethics
- CS 239 Ethics for AI, Robotics, and Human-Robot Interaction
Probability, Statistics, and Mathematics
- EE 104 Probabilistic Systems Analysis, or
- Math 165 Probability, or
- Math 166 Statistics, or
- EE 140 Stochasic Processes, Detection, and Estimation
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The MSAI – Computer Science track focuses on the underpinnings and applications of machine learning and artificial intelligence from a computational perspective, with a strong emphasis on the broader social context in which AI technologies are developed and deployed.
Required Course
- AI Foundations and Knowledge Representations
Breadth Electives (Pick four)
- CS 119 Big Data
- CS 132 Computer Vision
- CS 133 Human-Robot Interaction
- CS 136 Statistical Pattern Recognition
- CS 137 Deep Neural Networks
- CS 138 Reinforcement Learning
- CS 141 Probabilistic Robotics for HRI
- CS 142 Network Science
- CS 143/EE 130 Distributed ML and Control
- CS 157 Special Topics in AI
- CS 166 Computational Systems Biology
- CS 167/BME 167 Computational Biology
- CS 168/EE 109 Convex Optimization or CS 268/EE 159 Advanced Optimization
- CS 169 Statistical Bioinformatics
- CS 236 Computational Learning Theory
- Math 123 Mathematical Aspects of Data Analysis
- Up to one additional course on ethics and social impact, such as:
- CS 155 Special Topics in Social Context of Computing
- CS 182/DHP P236 Cyber in the Civilian Sector: Threats and Upheavals
- CS 183/DHP P237 Privacy in the Digital Age
- CS 184/ILO 184 Cyberlaw and Cyberpolicy
- CS 185 Computing for Developing Regions
- EE 185 Societal Aspects of Design
- DS 143/ME 173 Data Science for Sustainability
General Elective (Pick one)
- Any CS, DS, EE, or Math course numbered 100 or above, or as approved by an advisor.
- Student who wish to complete a two-term thesis or capstone project will replace the general elective, along with one elective from the AI breadth category, with said project work.
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The MSAI – Electrical and Computer Engineering track integrates underpinnings of machine learning and artificial general intelligence with specialized engineering domain knowledge, covering both fundamental and systems concepts in AI and how to apply these methods to diverse domains.
Required Course
- EE 141 Trusted and Responsible AI
Theory/Systems Electives (Pick four)
- CS 136 Statistical Pattern Recognition
- CS 137 Deep Neural Networks
- CS 236 Computational Learning Theory
- EE 109/CS 168 Convex Optimization or EE 159/CS 268 Advanced Optimization
- EE 127 Information Theory
- EE 130/CS 143 Distributed ML and Control
- EE 140 Stochastic Processes
- EE 152 Real-time Embedded Systems
- EE 153 Networked Embedded Systems
- EE 155 Parallel Computing
- EE 157 Emerging Memory Systems
- EE 193 High-dimensional Probability
- EE 193 Hardware and Systems for Machine Learning
- Math 123 Mathematical Aspects of Data Analysis
- Probabilistic Machine Learning
- Causal Inference
- Dynamic Programming
- Graph Neural Networks and Graph Signal Processing
Domain-specific Electives (Pick two)
- CS 119 Big Data
- CS 132 Computer Vision
- CS 133 Human-Robot Interaction
- CS 138 Reinforcement Learning
- CS 141 Probabilistic Robotics for HRI
- CS 142 Network Science
- CS 166 Computational Systems Biology
- CS 167/BME 167 Computational Biology
- CS 169 Statistical Bioinformatics
- EE 107 Communication Systems
- EE 108 Wireless Communications
- EE 114 Physics of Solar Cells
- EE 127/CS 149 Information Theory
- EE 129 Computer Communication Networks
- EE 193 Digestible Electronics
- EE 247 Advanced Analog and Mixed Signal IC Design
- Physics-guided Neural Networks
- Foundation Models for Non-text and Non-image applications
- Large Language Models
Gain Real-World Experience
Students will develop an ability to understand, implement, and deploy a wide range of AI technologies across disciplines, and they'll have the opportunity to work closely with some of the most renowned experts in the country. Gain hands-on experience through projects, co-ops, and internships, all while seamlessly transitioning from graduate studies to a full-time career.
Tufts School of Engineering's Graduate Cooperative Education (Co-Op) Program provides students with the opportunity to apply the advanced skills they have learned in their coursework to real-world engineering projects. Gain up to six months of full-time work experience, build your resume, and develop a competitive advantage for post-graduation employment.
Join a Growing Industry
In recent years, the prevalence of AI has skyrocketed, with a wide range of industries adopting AI technology. The U.S. Bureau of Labor Statistics estimates that the field has grown 28% annually since 2014. According to job listings at ziprecruiter.com, AI Engineering positions had an average salary of $106,386 in 2024.
What do you get at Tufts? A rigorous engineering education in a unique environment that blends the intellectual and technological resources of a world-class research university with the strengths of a top-ranked liberal arts college. Tufts University is one of the nation’s top research universities, earning a "tier 1" classification from the Carnegie Foundation and membership within the Association of American Universities (AAU). We are located a short subway ride from Boston and Cambridge, a world-renowned cities full of boundary-pushing tech companies and job opportunities.