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 should apply for admission at least one semester before their expected graduation date, but are encouraged to apply as soon as they have taken the prerequisite classes for the minor.  Grades from the core courses, 10-301/-315/-401/-601 and 36-401, are also welcomed with the application.  An admission decision will usually be made within one month.

Requirements for the Minor


Grade Requirements:

  • All courses for the ML Minor, including pre-requisites, must be passed with at least a C.
  • The core courses (10-301/-315/-401/-601 and 36-401) must average to at least a 3.0 (i.e., 1 A and 1 C, or 2 Bs).
  • The student's overall, university-wide QPA must remain at least 2.5.

Prerequisites:

  • CS background:  15-122 Principles of Imperative Computation
  • One year calculus: 21-120 and 21-122 or equivalent
  • Probability & statistics:
    • Option 1: (36-217, 36-218, 36-225, or 21-325) plus (36-226 or 36-326)
    • Option 2: 36-218 with at least a B

Double Counting Restrictions:

No course in the Machine Learning Minor may be counted towards another SCS minor.  Additionally, at least 3 courses (each being at least 9 units) must be used for only the Machine Learning Minor, not for any other major or minor. (These double counting restrictions apply specifically to the Core Courses and the Electives. Prerequisites may be counted towards other SCS minors and do not count towards the 3 courses that must be used for only the Machine Learning Minor.)

Curriculum

Core Courses - 21 units:

  • 10-301 (formerly 10-601) or 10-315 (formerly 10-401) Introduction to 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 - 3 courses (each being at least 9 units) from the options below:

  • A year-long senior research project in machine learning
  • An approved graduate-level machine learning course
  • An advanced data analysis courses from statistics
  • A combination of two related courses, 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

These options are explained in detail on the ML Minor Electives page.

Administration

The ML Director of Undergraduate Studies is Professor Matt Gormley and the ML Minor Program Coordinator is Dorothy Holland-Minkley. They can both be reached at ml-minor@cs.cmu.edu. Please contact them about eligibility, curriculum, etc.

How To Apply

Complete the Machine Learning Minor Application Google form. It asks for your contact information, basic information about your academic history, a proposed schedule of the courses you're planning to take for the Machine Learning Minor (which can be changed later), and a brief (150-250 word) Statement of Purpose describing your reasons for pursuing the ML Minor. Admissions decisions are usually made within one month.