Machine Learning Electives
All 12-unit courses from the School of Computer Science or Department of Statistics & Data Science at the 700-level or above are pre-approved for Machine Learning MS & PhD students, as are all courses in the Menu Core.
This page highlights some electives that may be of particular interest, and also adds some additional pre-approved courses at the 600-level (for MS students) or outside SCS. It also indicates some 6-unit mini-courses, where two mini-courses can be taken to count for one full 12-unit elective.
Students who want to count a course, as an elective, outside of SCS/Statistics or not on the suggested list below should consult with their advisor. Students should consider whether the course contains technical and mathematical content that will help in learning and applying machine learning. Students are also welcome to take courses beyond the electives that are required by the program.
- 02-710 Computational Genomics
- 10-605/-805 Machine Learning with Large Datasets
- 10-709 Fundamentals of Learning from the Crowd
- 10-808 Language Grounding to Vision & Control
- 10-830/90-904 Machine Learning in Policy
- 11-641 Machine Learning for Text Mining
- 11-642 Search Engines
- 11-661/11-761 Language and Statistics
- 11-688 Computational Forensics and Investigative Intelligence
- 11-704 Information Processing and Learning
- 11-711 Algorithms for NLP
- 11-727 Computational Semantics for NLP
- 11-751 Speech Recognition and Understanding
- 11-755 Machine Learning for Signal Processing
- 11-785 Intro to Deep Learning
- 11-791 Design & Engineering of Intelligent Information Systems
- 15-615 Database Applications
- 15-619 Cloud Computing
- 15-640 Distributed Systems
- 15-650 Algorithms & Advanced Data Structures
- 15-651 Algorithm Design & Analysis
- 15-719 Advanced Cloud Computing
- 15-855 Introduction to Computational Complexity Theory
- 15-857 Analytical Performance Modeling & Design of Computer Systems
- 15-887 Planning, Execution and Learning
- 16-720 Computer Vision
- 16-811 Mathematical Fundamentals for Robotics
- 16-824 Visual Learning & Recognition
- 16-843 Manipulation Algorithms
- 16-868 Biomechanics & Motor Control
- 18-755 Networks in the Real World
- 36-754 Adv. Probability
- 36-759 Statistical Models of the Brain
- 80-816 Causality and Learning
(must take two for a total of 12 units)
- 11-714 Tools for NLP, 6 units
- 36-720 Network Models, 6 units
- 36-723 Hidden Markov Models: Theory & Applications, 6 units
- 36-763 Hierarchical Models, 6 units
- 36-794 Astrostatistics, 6 units