M.S. in Artificial Intelligence

The Master of Science in Artificial Intelligence - Electrical Engineering is a three-semester (97 unit) program that offers the opportunity to learn state-of-the-art knowledge of artificial intelligence from an engineering perspective. Today AI is driving significant innovation across products, services, and systems in every industry, and tomorrow’s AI engineers will have the advantage.

ECE students within the program will learn how to design and build AI-orchestrated systems capable of operating within engineering constraints. At Carnegie Mellon, we are leading this transformation by teaching students how to simultaneously design a system’s functionality and supporting AI mechanisms, including both its AI algorithms and the platform on which the AI runs, to produce systems that are more adaptable, resilient, and trustworthy.

Students pursuing the M.S. in AI Engineering in ECE will be able to:

• Demonstrate knowledge of artificial intelligence methods, systems, tool chains, and cross-cutting issues including security, privacy, and other ethical, societal, and policy challenges
• Apply ECE concepts and tools in enabling AI systems and producing AI tools
• Be informed practitioners of AI methods to solve ECE and related problems, applying ECE domain knowledge whenever possible to enhance AI effectiveness
• Understand the limits of AI systems and apply these techniques within these limits
• Evaluate trade-offs involving technical capabilities and limitations, policy, and ethics in artificial intelligent systems

Requirements

Students with a bachelor’s degree in electrical and computer engineering or a related discipline with an interest in the intersection of AI and engineering are encouraged to apply to this program.

Interested students should be able to demonstrate proficiency in:

• Programming (Python preferred) for data analysis
• Probability/statistics such as probability distributions, joint and conditional probability, independence, marginalization, Bayes rules, and maximum likelihood estimation
• Linear algebra topics such as matrix operations, linear transformations, projections, matrix derivatives, and eigendecomposition

Relevant Curriculum

The M.S. in Artificial Intelligence Engineering - Electrical and Computer Engineering curriculum includes Introduction to Graduate Studies, the College of Engineering core, and a course each from the ECE Enablers, Producers, and Consumers categories along with 18 units of restricted technical electives as detailed below. Additional course offerings are currently under development.

1 unit of ECE Introduction to Graduate Studies (18-989)

42 units taken from College of Engineering core courses (additional courses are under development).*Note that all M.S. AI-ECE students must enroll in two core courses during their first semester in the program

• Systems and Tool Chains for AI Engineering (12 units):  course number available soon
• Introduction to Machine Learning for Engineers can be fulfilled by taking one of the following courses:  18-661 (12 units) or 24-787 (12 units)
• Introduction to Deep Learning for Engineers can be fulfiled by taking one of the following courses:  18-780 (6 units), or 18-786 (12 units), or 24-788 (6 units) *Note that 18-780 is the first half of 18-786
• Trustworthy and Ethical AI Engineering:  24-784 (12 units)

12 units taken from ECE Enablers category

• Foundations of Computer Systems (18-613)
• Hardware Arithmetic for Machine Learning (18-640)
• Research Project for up to 15 units

12 units taken from ECE Producers category

• Optimization (18-660)
• Principles and Engineering Applications of AI (18-662)
• Advanced Probability & Statistics for Engineers (18-665)
• Algorithms for Large-Scale Distributed Machine Learning and Optimization (18-667)
• Applied Stochastic Processes (18-751)
• Estimation, Detection, and Learning (18-752)
• Special Topics - Graph Signal Processing and Learning (18-898D)
• Research Project for up to 15 units

12 units taken from ECE Consumers category

• Data Analytics for the Semiconductor Industry (18-663)
• Advanced Digital Signal Processing (18-792)
• Image and Video Processing (18-793)
• Speech Recognition and Understanding (18-781)
• Machine Learning for Signal Processing (18-797)
• Computer Vision (under development)
• Case Studies in AI/ML (under development)
• Research Project for up to 15 units

M.S. AI-ECE students may take up to 27 project units that can count toward their degree requirements. These units can be applied to the Enablers, Producers, and Consumers categories. However, note that only 15 units of M.S. Graduate Project can be taken in any given semester.

18 units of additional MS coursework 

The remaining courses can be fulfilled by any 18-6XX course or above.

In addition, 12 of these of 18 units can be from the following programs (shown under their parent college) or individually approved courses. *Note that courses must be 600 level or above:

College of Engineering

• Carnegie Institute of Technology (CIT) (39)
• Biomedical Engineering (42)
• Chemical Engineering (06)
• Civil & Environmental Engineering (12)
• Engineering & Public Policy (19)
• Information Network Institute (14)
• Integrated Innovation Institute (49)
• Materials Science & Engineering (27)
• Mechanical Engineering (24)
• CMU Africa (04)

Dietrich College of Humanities and Social Sciences

• Statistics (36)
• Center for the Neural Basis of Cognition (86)
• Heinz School of Information Systems (95)
• Heinz College-Wide Courses (94)

Mellon College of Science (MCS)

• Biological Sciences (03)
• Chemistry (09)
• Mathematical Sciences (21)
• Physics (33)

School of Computer Science (SCS)

• Computational Biology (02)
• Computer Science (15)
• Entertainment Technology Center (53)
• Institute for Software Research (08)
• Robotics Institute (16)
• Human-Computer Interaction Institute (05)
• Language Technologies Institute (11)
• Machine Learning (10)
• Software Engineering (17)

Tepper School of Business (TEP)

• Tepper School of Business (45)

Additional courses outside of these programs that are approved to be counted toward General Technical Elective Coursework:

• 46-926, 46-929
• 47-830, 47-834
• 51-882
• 57-947, 57948
• 80-713
• 84-688
• 85-705, 85-777
• 90-756, 90-808
• 93-711
• 99-783

For students interested in pursuing a Summer internship - 3 units of Internship for Electrical and Computer Engineering MS Students (18-994) may be used toward the 18 units of additional MS coursework.

Maximum units allowed

Endless Opportunities

Whether pursuing academia or industry, this degree uniquely positions students for the future of research and high-demand careers with a mastery of integrating engineering domain knowledge into AI solutions.

For additional information about this college-wide initiative, please visit the College of Engineering's Master's of AI Engineering website.