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Graduate Minor in Artificial Intelligence

Artificial intelligence is transforming every industry, from engineering and healthcare to finance, manufacturing, robotics, and beyond. Organizations are seeking professionals who can leverage AI and machine learning to solve complex problems, automate processes, and drive innovation. The Graduate Minor in Artificial Intelligence provides graduate students with the knowledge and practical skills needed to integrate AI into their primary field of study, preparing them to lead in an increasingly AI-driven world.

The three-course minor combines a strong foundation in machine learning and deep learning with the flexibility to explore AI applications relevant to each student's discipline. By completing the minor, students not only gain technical expertise in modern AI methods, but also earn an interdisciplinary credential that distinguishes them in the job market and demonstrates AI competency to employers and graduate research programs.

Why earn the AI Minor?

  • Develop in-demand skills in artificial intelligence and machine learning.
  • Complement your graduate degree with expertise valued across virtually every engineering discipline.
  • Learn the foundations and applications of machine learning, deep learning, and large language models.
  • Customize the program with AI electives aligned with your academic and career interests.
  • Earn an official transcript notation that demonstrates AI competency and strengthens your professional credentials.

How to Apply

The Graduate Minor in Artificial Intelligence is open to currently enrolled McCormick master's students beginning every Fall quarter. To apply, complete the online application form.

Applications are reviewed by the AI Minor Committee. Students are encouraged to apply early in their graduate program so they can incorporate the required coursework into their degree plan.

Requirements

The Graduate Minor in Artificial Intelligence consists of 3 course units. Students complete one course from each of the following three groups, which includes two foundational courses and one AI elective in their own discipline. All courses must be taken for a letter grade. A minimum grade of B in each course is required to receive the Graduate Minor in Artificial Intelligence notation on their transcript.

Students enrolled in the graduate minor program may double count any course above the TGS minimum requirement of 9 units. For example, if the program requires 12 units, TGS MS programs may allow up to 3 courses used toward the graduate minor to count toward the MS degree as well. However, TGS MS students should check with their departments about double-counting rules, as departments may have a stricter policy.

View TGS Minor Requirements

Group A: Machine Learning Foundations (1 unit)

  • COMP_SCI 349: Machine Learning
  • ELEC_ENG 475: Machine Learning: Foundations, Applications, and Algorithms

Group B: Applied & Advanced AI (1 unit)

  • ELEC_ENG 435: Deep Learning Foundations from Scratch
  • COMP_SCI 449: Deep Learning
  • COMP_SCI 461: Large Language Models

Group C: AI Elective (1 unit)

  • COMP_ENG 395: Embedded Artificial Intelligence
  • COMP_ENG 495: Machine Learning and Artificial Intelligence for Robotics
  • COMP_ENG 495: AI for Science and Business
  • COMP_ENG 495: AI Innovation Lab
  • COMP_SCI 348: Introduction to Artificial Intelligence
  • COMP_SCI 337: Natural Language Processing
  • COMP_SCI 496: Foundations of Reliable Machine Learning
  • COMP_SCI 496: Theoretical Foundations of Data Science
  • COMP_SCI 344: Design of Computer Problem Solvers
  • COMP_SCI 371: Knowledge Representation and Reasoning
  • COMP_SCI 325: AI Programming
  • COMP_SCI 396: Declarative Programming for Game AI
  • COMP_SCI 496: Logic in AI
  • COMP_SCI 396: Reasoning and Planning in the Foundation Model Era
  • COMP_SCI 496: Agent AI
  • COMP_SCI 347: Conversational AI
  • COMP_SCI 497: Explanation and reproducibility in data-driven science
  • COMP_SCI 301: Introduction to Robotics Laboratory
  • COMP_SCI 353: Natural and Artificial Vision
  • COMP_SCI 396: Machine Learning and Sensing
  • COMP_SCI 469: ML and AI for Robotics
  • DSGN 395: Designing with AI
  • ELEC_ENG 332: Introduction to Computer Vision
  • ELEC_ENG 432: Advanced Computer Vision
  • ELEC_ENG 495: Machine Learning for Medical Images and Signals
  • ELEC_ENG 495: Optimization Techniques for Machine Learning and Deep Learning
  • ELEC_ENG 495: Scientific Machine Learning
  • ELEC_ENG 473: Deep Reinforcement Learning from Scratch
  • ES_APPM 345: Applied Linear Algebra
  • ES_APPM 405-1: Statistics and Data Science
  • ES_APPM 445: Advanced Numerical Methods for Linear Algebra
  • ES_APPM 479: Data-Driven Methods for Dynamical Systems
  • IEMS 301: Introduction to Statistical Learning
  • IEMS 304: Statistical Learning for Data Analysis
  • IEMS 305: Foundations of Modern Machine Learning
  • IEMS 351: Optimization Methods in Data Science
  • IEMS 402: Statistical Learning
  • IEMS 469: Advanced Algorithms for Machine Learning
  • IEMS 490: Advanced Topics in Large Foundation Models
  • IEMS 490: Deep Generative AI
  • IEMS 490: Theory and Algorithms for LLMs
  • MECH_ENG 447: AI in Manufacturing Data Analytics
  • MECH_ENG 455: Active Learning in Robotics
  • MECH_ENG 495: Sensing, Navigation, and Machine Learning for Robotics
  • MECH_ENG 495: Machine Learning for Experimental Mechanics
  • MECH_ENG 495: AI Assisted Research in Science and Engineering
  • MEM 410: Managerial Analytics
  • MEM 411: Marketing Analytics