Carnegie Mellon University

M.S. in Artificial Intelligence

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The Master of Science in Artificial Intelligence - Electrical and Computer 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

Admission 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

Curriculum Requirements

The M.S. in Artificial Intelligence Engineering - Electrical and Computer Engineering curriculum includes the following:

  • 42 units of ECE AI Core Courses
  • 36 units of ECE AI Domain Courses
  • 18 units of General Elective Courses
  • 1 unit of Introduction to Graduate Studies (18-989)
42 units must be taken from the following College of Engineering core courses. One course must be completed from each of the four areas.
*Note that all M.S. AI-ECE students must enroll in two core courses during their first semester in the program.

AI Systems 
  • 18-763 Systems and Tool Chains for AI Engineering (12 units)

Machine Learning

  • 18-661 Introduction to Machine Learning for Engineers (12 units)

Deep Learning

  • 18-780 Introduction to Deep Learning Part I (6 units) OR
  • 18-790 Introduction to Deep Learning and Pattern Recognition for Computer Vision Part I (6 units)

*Note that 18-780 is the first half of 18-786 (12 units) and 18-790 is the first half of 18-794 (12 units), so 18-786 or 18-794 satisfies this Deep Learning requirement. The additional 6 units earned by taking 18-786 or 18794 would be counted towards the General Elective requirement. 

AI Ethics

  • 24-784 Trustworthy and Ethical AI Engineering (12 units)

36 total units must be taken from the following list of ECE AI Domain courses.
You may satisfy this requirement by taking EITHER:

  • 12 units from each of the three domains
  • 24 units from one domain and 12 units from a second domain

Enablers Domain

  • Foundations of Computer Systems (18-613)
  • Hardware Arithmetic for Machine Learning (18-640)
  • Research Project for up to 12 units (18-983)

Producers Domain

  • 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 12 units (18-984)

Consumers Domain

  • 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)
  • Research Project for up to 12 units (18-985)

*Note: A maximum of 12 units of Research Project courses (18-983, 18-984, 18-985) can be counted in the ECE AI Domain Courses requirement.

18 units of General Elective courses must be taken as follows.

ECE Technical Elective 

  • 6 units must be fulfilled by any 18-6XX course or above. 

General Technical Elective

  • 12 units must be fulfilled by any course that is 600 level or above from the following approved departments.

*Please note: a maximum of 12 units of undergraduate coursework (XX-300 to XX-599) can qualify to be substituted toward the 18 units of General Elective coursework. Qualifying coursework must be offered by the same departments approved below.

College of Engineering

  • Electrical and Computer Engineering (18)
  • 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.

1 unit of Introduction to Graduate Studies (18-989) must be taken.

This course must be completed in your first semester. 

M.S. AI-ECE students may take up to 27 project units that can count toward their degree requirements. Only 15 units of M.S. Graduate Project can be taken in any given semester.

A maximum of 12 project units can be applied to the ECE AI Domain course requirements. Students must get approval from the department for a project to fulfill either the Enablers (18-983), Producers (18-984), or Consumers (18-985) domain. Students can use the Student Project Tracker (SPT) website to register a project and work with their primary advisor for course approval and registration. 

*Please note that the Project Option is not available for ECE AI Students.

Maximum units allowed

Students who are pursuing the M.S. AI-ECE cannot register for their final semester if they have already completed 120 units of coursework. These units include courses taken for audit, pass/no pass, and withdrawal. Please refer to CIT policy on MS degree units for additional information.

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.