Minor in Machine Learning-Machine Learning Department - Carnegie Mellon University

Undergraduate Minor in Machine Learning

Machine learning and statistical methods are increasingly used in many application areas including natural language processing, speech, vision, robotics, and computational biology.  The Minor in Machine Learning allows undergraduates to learn about the core principles of machine learning.

Eligibility
Students must apply for admission after passing the two core courses listed below. An admission decision will usually be made within one month.

Requirements for the Minor

Prerequisites:

  • CS background:  15-122 Principles of Imperative Computation, (Must pass with a C for this class).
  • One year calculus: 21-120 and 21-122 or equivalent
  • One year of probability & statistics:  36-225 or 21-325, followed by 36-226

Double Counting Restrictions:
Any course in the Machine Learning minor, other than the pre-requisites, may not be counted towards another SCS minor.

Core Courses:

  • 10-601 Machine Learning or 10-701/15-781 Machine Learning
  • 36-401 Modern Regression (This course is often oversubscribed, especially for non-statistics majors. ML minors should be aware of this and register immediately when slots are available.)

Electives - Total of three courses (36 credits) from the options below:

  • A year-long senior project, supervised or co-supervised by a ML Faculty member. (Normally this will be conducted as two semester-long projects.)
  • Any core ML course, including 10-702 Statistical Machine Learning; 10-705 Intermediate Statistics; 15-826 Multimedia Databases and Data Mining; 15-853 Algorithms in the Real World.
  • 10-605 Machine Learning with Large Datasets.
  • 15-859 (B) Machine Learning Theory.
  • Any advanced data analysis courses from statistics, including 36-402 Advanced Data Analysis; 36-315 Statistical Graphics and Visualization; or courses from the 36-46x series (such as 36-461 Statistical Methods for Epidemiology, 36-462 Chaos, Complexity and Inference, 36-463 Hierarchical Models, and 36-464 Multivariate Methods.)
  • A combination of two related courses, from the minor electives page, where one provides an introduction to a field that uses machine learning methods, and the second is in the same discipline and includes a significant machine-learning component.

Students should note that several of these courses are primarily aimed at graduate students, and should make sure that they are adequately prepared for them before enrolling.

Administration
The ML Director of Undergraduate Studies is William W. Cohen (ml-minor@cs.cmu.edu). Please contact him about eligibility, curriculum, etc. He will also help match students with faculty for projects.

How To Apply

Send email to William Cohen (ml-minor@cs.cmu.edu), include the following:

  • Your full name
  • Andrew ID
  • Preferred email address (if different)
  • The semester you intend to graduate
  • Your QPA;
  • All (currently) declared majors and minors, or your home college if no major declared. 
  • A one page statement of purpose describes why you want to take this minor, and how it relates to your career goals. 
  • A proposed schedule of the courses you plan to take to satisfy the minor. (The plan is not a commitment and can be revised later.)