Specialization in Machine Learning
For a Master of Science in Computer Science, Specialization in Machine Learning (15 hours), students must select from the following:
*The following is a complete look at the courses that may be selected to fulfill the Machine Learning specialization, regardless of campus; only courses listed with bold titles are offered through the online program.
Core Courses (6 hours)
Algorithms: Pick one (1) of:
- CS 6505 Computability, Algorithms, and Complexity
- CS 6515 Introduction to Graduate Algorithms
- CS 6520 Computational Complexity Theory
- CS 6550 Design and Analysis of Algorithms
- CS 7510 Graph Algorithms
- CS 7520 Approximation Algorithms
- CS 7530 Randomized Algorithms
- CSE 6140 Computational Science and Engineering Algorithms
And, pick one (1) of:
- CS 7641 Machine Learning
- CSE 6740 Computational Data Analysis: Learning, Mining, and Computation
Electives (9 hours)
Elective ML courses must have at least 1/3 of their graded content based on Machine Learning.
Pick three (3) of:
- CS 6220 Big Data Systems & Analysis
- CS 6476 Computer Vision
- CS 6603 AI, Ethics, and Society
- CS 7280 Network Science
- CS 7535 Markov Chain Monte Carlo
- CS 7540 Spectral Algorithms
- CS 7545 Machine Learning Theory
- CS 7616 Pattern Recognition
- CS 7626 Behavioral Imaging
- CS 7642 Reinforcement Learning and Decision Making (Formerly CS 8803-O03)
- CS 7643 Deep Learning
- CS 7644 Machine Learning for Robotics
- CS 7646 Machine Learning for Trading
- CS 7650 Natural Language
- CS 8803 Special Topics: Probabilistic Graph Models
- CSE 6240 Web Search and Text Mining
- CSE 6242 Data and Visual Analytics
- CSE 6250 Big Data for Health (Formerly CSE 8803)
- ISYE 6416 Computational Statistics
- ISYE 6420 Bayesian Methods
- ISYE 6664 Stochastic Optimization
Free Electives (15 hours)
Free electives may be any courses offered through the OMSCS program. You can find the list of current OMSCS courses here.