MS in Machine Learning Engineering

Program Plan: MS in Machine Learning Engineering

Cores Courses (4 courses, 12 credits)

Course Title Prereq
ECE 610 Machine Learning & Artificial Intelligence ECES 521
ECE 612 Applied Machine Learning Engineering
ECE 687 Pattern Recognition
ECES 521 Probability & Random Variables

Aligned Mathematical Theory (choose 2 courses, 6 credits)

Course Title Prereq
ECES 522 Random Process & Spectral Analysis ECES 521
ECES 523 Detection & Estimation Theory ECES 521
ECES 811 Optimization Methods for Engineering Design  
ECET 602 Information Theory and Coding ECES 521
MATH 504 Linear Algebra & Matrix Analysis  
MATH 510 Applied Probability and Statistics I  

Applications (choose 1 course, 3 credits)

Course Title Prereq
ECE 686 Cell & Tissue Image Analysis  
ECES 620 Multimedia Forensics and Security ECES 521
ECES 641 Bioinformatics  
ECES 650 Statistical Analysis of Genomics  
ECES 660 Machine Listening and Music IR ECES 631

Signal Processing (choose 1 course, 3 credits)

Course Title Prereq
ECES 631 Fundamentals of Deterministic Digital Signal Processing  
ECES 681 Fundamentals of Computer Vision  
ECES 682 Fundamentals of Image Processing ECES 631

Engineering Electives (choose 3 courses [9 credits] from within the College of Engineering)

Transformational Electives (choose 2 elective courses [6 credits] that promote the development of leadership, communication, and ethics)

Course Title Prereq
COM 610 Theories of Communication and Persuasion  
EDGI 510 Culture, Society & Education in Comp. Perspective  
EDGI 522 Education for Global Citizenship, Sustainability, and Social Justice (formerly EDGI 512)  


  • Thesis Option: A minimum of two terms of laboratory-based research (6 credits) that leads to a publicly defended MS thesis. Students will be advised by a faculty member, and when applicable, a representative of industry or government sponsor.
  • Non-thesis Option: In lieu of research and thesis, students will complete six credits of additional coursework in Aligned Mathematical Theory, Applications, or Signal Processing. Graduate Co-op is encouraged for non-thesis students, but is not required.