• Core 1: Machine Learning
  • EGN5216 Machine Learning for AI Systems (3 credits)
    (Typically offered in Fall)
• Core 2: AI Systems
  • EGN 6216 Artificial Intelligent Systems (3 credits)
    (Typically offered in Fall)
• Core 3: Sensing & Analysis (select 1 of these 3 options).
  • CAP 5416 Computer Vision (3 credits)
    (Typically offered in Fall)
  • EEL 5406 Computational Photography (3 credits)
    (Typically offered in Fall)
  • EEE 6512 Image Processing and Computer Vision (3 credits)
    (Typically offered in Spring)

• Core 4: Security (select 1 of these 3 options)
  • EEL 5739 IoT Security and Privacy (3 credits)
    (Typically offered in Spring)
  • CIS 6261 Trustworthy Machine Learning (3 credits)
    (Typically offered in Fall)
• CORE 5: Deep Learning (select 1 of these 2 options)
  • EGN 6217 Applied Deep Learning (3 credits)
    (Typically offered in Spring)
• CORE 6: Ethics (select 1 of these 2 options)
  • EGS 6216 AI Ethics for Tech Leaders (3 credits)
    (Typically offered in Fall & Spring)
  • LAW 6930 Legal, Policy, and Ethical Dimensions (3 credits)
    (Typically offered in Fall on LAW school schedule)

Choose three (3) elective courses, at least one (1) in AML-DDM and one (1) in AR-HCC:

Advanced Machine Learning and Data Driven Modeling (AML-DDM)

• BME 6938 Biomedical Data Science (3 credits)
  (Typically offered in Fall)
• EEL 5840 Fundamentals of Machine Learning (3 credits) or STA 6703 Statistical Machine Learning (3 credits)
  (Typically offered in Fall & Spring or Typically offered in Fall)
• CAP 6617 Advanced Machine Learning (3 credits)
  (Typically offered in Fall)
• EEL 6814 Neural Networks and Deep Learning (3 credits)
  (Typically offered in Fall)
• EEL 6825 Pattern Recognition and Intelligent Systems (3 credits)
  (Typically offered in Fall)
• EEE 6504 Machine Learning for Time Series (3 credits)
  (Typically offered in Spring)

Autonomy, Robotics, and Human-Centered Computing (AR-HCC)

• ABE 6005 Applied Control for Automation and Robotics (3 credits)
  (Typically offered in Spring)
• CAP 5108 Research Methods for Human Centered Computing (3 credits)
  (Typically offered in Spring)
• EML 6351 Adaptive Control (3 credits)
  (Typically offered in Spring)

Unrestricted Technical Electives (UT)

This group allows the students to take a technical elective course for greater curriculum flexibility. The technical elective courses in this group must be chosen in coordination with the graduate advisor to ensure prerequisite fulfillment and to optimize for achieving student career goals (e.g., courses related to entrepreneurship).

Pre-approved courses:

• EGN 6951 Integrated Product and Process Design G1 (3 credits)
  (Typically offered in Fall)
• EGN 6XXX Practical Work in AI Systems (3 credits)
  (Available in Summer, Fall & Spring)
• BME 5703 Statistical Methods for Biomedical Engineering (3 credits)
  (Typically offered in Spring)
• BME 6522 Biomedical Multivariate Signal Processing (3 credits)
  (Typically offered in Spring)
• ABE 6933 App Stat Machine Learning (3 credits)
  (Typically offered in Fall)
• ABE 6933 Computer Vision& Deep Learning (3 credits)
  (Typically offered in Fall)
• CAP 5100 Human-Computer Interaction (3 credits)
  (Typically offered in Spring)
• EEL 5934 Intro to Quantum Devices and Quantum Engineering (3 credits)
  (Typically offered in Spring)
• EEL6871 Cloud Computing Systems Management (3 credits)
  (Typically offered in Fall)
• CAP 5510 Bioinformatics (3 credits)
  (Typically offered in Fall)
• EML 6350 Introduction to Nonlinear Control (3 credits)
  (Typically offered in Fall)
• EML 6048 Machine Learning and System Control (3 credits)
  (Typically offered every other Spring, next available Spring 2027)
• EML 6577 Verification, Validation and Uncertainty Quantification and Reduction (3 credits)
  (Typically offered every 3rd semester, next available Fall 2025)
• EML 6350 Introduction to Nonlinear Control (3 credits)
  (Typically offered in Fall)
• EML 6352 Optimal Estimation and Kalman Filtering (3 credits)
  (Typically offered in Spring)

• CAI 6826 Project in AI Systems (Non-Thesis Project) (3 credits)
  (Typically offered in Spring and Fall)