Carnegie Mellon University

Machine Learning Minor Electives

Students must take at least 36 units of electives in Machine Learning. Students should note that many of these courses are aimed at graduate students, and so should make sure that they are adequately prepared for them before enrolling.

Take as many of these courses as desired, in any order:

  • 10-605/-805 Machine Learning with Large Datasets (12 units)
  • 10-701 Machine Learning (12 units)
  • 10-702 Statistical Machine Learning (12 units)
  • 10-703/-707 Deep Learning (12 units)
  • 10-802 Analysis of Social Media
  • 15-826 Multimedia Databases and Datamining (12 units)
  • 15-853 Algorithms in the Real World (12 units)
  • 15-859 Section B: Machine Learning Theory (12 units)
  • 36-315 Statistical Graphics and Visualization (9 units)
  • 36-402 Advanced Data Analysis (9 units)
  • 36-461 Statistical Methods in Epidemiology (9 units)
  • 36-462 Data Mining (9 units)
  • 36-463 Multilevel and Hierarchical Models (9 units)
  • 36-464 Applied Multivariate Methods (9 units)
  • 36-700/-705 Statistics (12 units)

Courses outside of this list may be allowed, but need prior approval from the Machine Learning Minor Director.

Senior Research consists of 2 semesters of 10-500 Senior Research, totalling 24 units.

The research must be a year-long senior project, supervised or co-supervised by a Machine Learning Core Faculty member. It is normally conducted as two semester-long projects. Some samples of available Machine Learning Senior Projects are available here.

Interested students should contact the faculty they wish to advise them to discuss the research project, before the semester in which research will take place. Once both student and advisor agree upon a project, the student should submit a one-page research proposal to the Machine Learning Minor Director to confirm that the project will count for the Machine Learning Minor. The student should expect to meet with the Minor Director during both Senior Fall and Spring to discuss the project, and will present the work and submit a year-end write-up to the Minor Director at the end of Senior year.

Students are encouraged to reach out to the Minor Director with questions at any time. The Minor Director can also help match students with faculty for research projects.

Two-Course Sequences

Two-course sequences are available in the following areas. For some, a specific introductory course must be taken followed by a choice of additional electives. For others, a specific pair of courses may be required, or any courses from the list may be used. These courses may only be used as Machine Learning Minor Electives if two courses from the same sequence are taken.

It may be possible for a different course to be used as part of a two-sequence pair, particularly if desired courses aren't being offered. Students should email the Machine Learning Minor Director with a course description and an explanation to ask permission for such a substitution.

Introductory Course:

  • 15-381 Artificial Intelligence: Representation and Problem Solving (9 units)

Plus one of:

  • 15-388/-688 Practical Data Science (9/12 units)
  • 15-780 Graduate Artificial Intelligence (12 units)
  • 15-887 Planning, Execution, and Learning (12 units)
  • 15-892 Foundations of Electronic Marketplaces (12 units)

Take both courses in Computational Genomics:

  • 02-510 Computational Genomics (12 units)
  • 03-511 Computational Molecular Biology and Genomics (9 units)

Or take both courses in Biological Modeling:

  • 02-530 Cell and Systems Modeling (12 units)
  • 03-512 Computational Methods for Biological Modeling and Simultation (9 units)

Introductory Course:

  • 08-801 Dynamic Network Analysis (12 units)


  • 08-810 Computational Modeling of Complex Socio-Technical Systems (12 units)

Introductory Course:

  • 16-311 Introduction to Robotics (12 units)

Plus one of:

  • 15-463 Computational Photography (12 units)
  • 16-720 Computer Vision (12 units)
  • 16-725 Medical Image Analysis (12 units)
  • 16-823 Physics-Based Methods in Computer Vision (Appearance Modeling) (12 units)
  • 16-824 Visual Learning and Recognition (12 units)

Introductory Course:

  • 11-411 Natural Language Processing (12 units)

Plus one of:

  • 11-441/-641/-741 Machine Learning for Text Mining (9/12 units)
  • 11-442/-642 Search Engines (12 units)
  • 11-731 Machine Translation and Sequence-to-Sequence Models (12 units)
  • 11-748 Information Extraction (12 units)
  • 11-751 Speech Recognition and Understanding (12 units)
  • 11-755 Machine Learning for Signal Processing (12 units)
  • 11-761 Language and Statistics (12 units)
  • 11-762 Language and Statistics II (12 units)
  • 11-763 Structured Prediction for Language and Other Discrete Data (12 units)
  • 11-773 Text-Driven Forecasting (12 units)

Choose any two of:

  • 15-386/-686 Neural Computation (9/12 units)
  • 15-883 Computational Models of Neural Systems (12 units)
  • 18-699/42-590 Neural Signal Processing (12 units)
  • 36-759 Statistical Models of the Brain (12 units)
  • 42-595 Neural Data Analysis (12 units)
  • 85-419/-719 Introduction to Parallel Distributed Processing (9/12 units)

Take both of:

  • 10-830 Research Seminar in Machine Learning and Policy (6 units)
  • 10-831 Special Topics in Machine Learning and Policy (6 units)

Note: If interested in this option, please contact the Machine Learning Minor Director to confirm that 10-831 Special Topics will be offered during an appropriate semester for you.

Introductory Course:

  • 16-311 Introduction to Robotics (12 units)

Plus one of:

  • 16-745 Dynamic Optimization (12 units)
  • 16-831 Statistical Techniques in Robotics (12 units)
  • 16-899 Section C: Adaptive Control and Reinforcement Learning (12 units)