Machine Learning Electives

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All 12-unit courses from the School of Computer Science or Department of Statistics and Data Science at the 700-level or above are preapproved for machine learning master's &and Ph.D. students, as are all courses in the menu core.

This page highlights some electives that may be of particular interest, and also adds additional preapproved courses at the 600-level (for master's students) or outside SCS. It also indicates some six-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 that is outside SCS/statistics or not on the suggested list below should consult their adviser. Students should consider whether the course contains technical and mathematical content that will help them learn and apply machine learning. Students are also welcome to take courses beyond the electives 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 and 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 and Engineering of Intelligent Information Systems
  • 15-615: Database Applications
  • 15-619: Cloud Computing
  • 15-640: Distributed Systems
  • 15-650: Algorithms and Advanced Data Structures
  • 15-651: Algorithm Design and Analysis
  • 15-719: Advanced Cloud Computing
  • 15-855: Introduction to Computational Complexity Theory
  • 15-857: Analytical Performance Modeling and Design of Computer Systems
  • 15-887: Planning, Execution and Learning
  • 16-720: Computer Vision
  • 16-811: Mathematical Fundamentals for Robotics
  • 16-824: Visual Learning and Recognition
  • 16-843 Manipulation Algorithms
  • 16-868: Biomechanics and Motor Control
  • 18-755: Networks in the Real World
  • 36-754: Advanced Probability
  • 36-759: Statistical Models of the Brain
  • 80-816: Causality and Learning
Mini courses are generally six units. Students who take mini courses to fulfill the elective requirement must take two (for a total of 12 units). Some examples of preapproved minis include:
  • 11-714: Tools for NLP
  • 36-720: Network Models
  • 36-723: Hidden Markov Models — Theory and Applications
  • 36-763: Hierarchical Models,
  • 36-794: Astrostatistics