Carnegie Mellon University

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.

Some Suggested Electives

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

MINI Courses (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