Carnegie Mellon University

M.S. in Artificial Intelligence

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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 is currently offered in two forms:  The M.S. Artificial Intelligence Engineering - ECE, Standard, and the M.S. Artificial Intelligence Engineering - ECE, Applied. Both options are intended to be completed in three semesters with 97 units of coursework. The Applied Study option includes a required internship to be completed in the summer semester. 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

  • Special Topics - Systems and Tool Chains for AI Engineering (18-813)
  • Introduction to Machine Learning for Engineers (18-661)
  • AI and Machine Learning for Engineers (24-787)
  • Special Topics - Deep Learning for Engineers (24-789)

12 units taken from ECE Enablers category (additional courses are under development)

  • Foundations of Computer Systems (18-613)
  • Special Topics - AI/ML and Cybersecurity (18-739C)

12 units taken from ECE Producers category (additional courses are under development)

  • 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)

12 units taken from ECE Consumers category (additional courses are under development)

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

18 units of additional MS Coursework 

The remaining courses can be fulfilled by any 18-6XX course or higher. In addition, 12 units of XX-6XX or higher outside of ECE may be taken according to the same rules as applied to the MS-ECE and MS-SE degrees. 

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.