Undergraduate Minor in Machine Learning

Home > Academics > 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

The Machine Learning Minor is open to undergraduate students in any major at Carnegie Mellon outside the School of Computer Science. (SCS students should instead consider the Machine Learning Concentration.) 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 are also welcomed with the application.An admission decision will usually be made within one month.

Requirements for the Minor

All courses for the ML Minor, including prerequisites, must be passed with a C or better.
  • CS background:  15-122
  • Math background: 15-151, 21-127, or 21-128
  • Probability & statistics background: 36-218, 36-219, 36-225, 36-235, 15-259, or 21-325
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, minor, or concentration. (These double counting restrictions apply specifically to the Core Courses and the Electives. Prerequisites may be counted towards other majors, minors, and concentrations and do not count towards the 3 courses that must be used for only the Machine Learning Minor.)

Curriculum

The Machine Learning Minor has 2 core courses that provide a foundation in the field:

  • 10-301 or 10-315 Introduction to Machine LearningOne of the following courses:
  • 10-403 Deep Reinforcement Learning & Control
  • 10-405 Machine Learning with Large Datasets 
  • 10-414 Deep Learning Systems: Algorithms and Implementation
  • 10-417 Intermediate Deep Learning 
  • 10-418 Machine Learning for Structured Data 
  • 10-422 Foundations of Learning, Game Theory, and Their Connections

The Machine Learning Minor requires at least 3 electives of at least 9 units each in Machine Learning. Students may select one of the following options to satisfy the electives requirement:

  • 3 Principal courses
  • 2 Principal courses + 1 Interdisciplinary course
  • 2 Principal courses + 1 semester of CS Senior Honors Thesis or Senior Research
  • 1 Principal course + 2 semesters of CS Senior Honors Thesis or Senior Research

Students should note that some of these elective courses (those at the 600-level and higher) are primarily aimed at graduate students, and so should make sure that they are adequately prepared for them before enrolling.

Graduate-level cross-listings of these courses can also be used for the ML Minor, if the student is adequately prepared for the more advanced version and the home department approves the student's registration.

  • 10-403/10-703 Deep Reinforcement Learning & Control
  • 10-405/10-605 Machine Learning with Large Datasets or 10-745 Scalability in Machine Learning
  • 10-414/10-714 Deep Learning Systems: Algorithms and Implementation
  • 10-417 Intermediate Deep Learning or 11-485 Introduction to Deep Learning or 10-707 Advanced Deep Learning
  • 10-418/10-618 Machine Learning for Structured Data or 10-708 Probabilistic Graphical Models
  • 10-422 Foundations of Learning, Game Theory, and Their Connections
  • 10-423/10-623 Generative AI
  • 10-424/10-624 Bayesian Methods in Machine Learning
  • 10-425/625 Introduction to Convex Optimization or 10-725 Convex Optimization
  • 10-613/10-713 Machine Learning Ethics and Society
  • 10-735 Responsible AI
  • 10-777 Historical Advances in Machine Learning
  • 36-401 Modern Regression
  • Other courses as approved

Note: Courses must come from separate lines. For example, if 10-417 Intermediate Deep Learning is used for the ML Minor, 11-485 Introduction to Deep Learning cannot also be used for the ML Minor.

  • 02-510 Computational Genomics
  • 03-511 Computational Molecular Biology and Genomics
  • 10-335 Art and Machine Learning
  • 10-737 Creative AI
  • 11-411 Natural Language Processing
  • 11-441 Machine Learning for Text Mining
  • 11-661 Language and Statistics
  • 11-731 Machine Translation and Sequence-to-Sequence Models
  • 11-751 Speech Recognition and Understanding
  • 11-755 Machine Learning for Signal Processing
  • 11-777 Multimodal Machine Learning
  • 15-281 Artificial Intelligence: Representation and Problem Solving
  • 15-386 Neural Computation
  • 15-388 Practical Data Science
  • 15-482 Autonomous Agents
  • 16-311 Introduction to Robotics
  • 16-385 Computer Vision
  • 16-720 Computer Vision
  • 16-745 Optimal Control and Reinforcement Learning
  • 16-824 Visual Learning and Recognition
  • 16-831 Statistical Techniques in Robotics
  • 17-537 Artificial Intelligence Methods for Social Good
  • 36-402 Advanced Methods for Data Analysis
  • 36-462 Special Topics: Data Mining
  • 36-463 Special Topics: Multilevel and Hierarchical Models
  • 36-700 Probability and Mathematical Statistics or 36-705 Intermediate Statistics
  • Other courses as approved

Note: Courses must come from separate lines. For example, if 36-700 Probability and Mathematical Statistics is used for the ML Minor, 36-705 Intermediate Statistics cannot also be used for the ML Minor.

The CS Senior Honors Thesis consists of 36 units of academic credit for this work, usually under the course number 07-599 SCS Honors Undergraduate Research Thesis. Up to 24 units (12 units each semester) may be counted towards the ML Minor. Students must consult with the Computer Science Department for information about the CS Senior Honors Thesis. Once both student and advisor agree upon a project, the student should submit a one-page research proposal to the Machine Learning Concentration Director to confirm that the project will count for the Machine Learning Concentration.

Senior research consists of 2 semesters of 10-500 Senior Research Project, totaling 24 units and counting as 2 electives.

The research must be a year-long senior project, supervised or co-supervised by a Machine Learning Core Faculty or Affiliated Faculty member. It is almost always conducted as two semester-long projects, and must be done in senior year.

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.

Your one-page research proposal should contain the following:

  • A working title, your name, and your advisor's name
  • The following 7 sections, using the section titles in bold below:
    1. Abstract (100 to 500 words)
    2. Motivation (why your research problem is important)
    3. Contributions (bulleted list of your research contributions)
    4. Related Work (brief mention of most relevant existing work)
    5. Expected Results (short description of likely outcomes)
    6. Timeline (detailed list of milestones over the next year)
    7. Bibliography
  • The signature of your research advisor, signifying endorsement of the project and willingness to supervise and evaluate it.

The student should email the ML Minor Director a brief update (two paragraphs) on their progress at the end of the Fall semester, and will present the work at the Meeting of the Minds 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 (ml-minor@cs.cmu.edu) with questions at any time.

Administration

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

Matt Gormley office hours for S25 registration are being held in GHC 8103 on Friday, 11/15/24, 2:30 - 3:00 pm, and Friday, 11/22/24, 2:00 - 2:30 pm.

Laura Winter holds office hours during Spring and Fall.

Fall office hours are being held on Thursdays, 2-3 pm in GHC 9112.  You can also email Laura at lwinter@andrew.cmu.edu with any questions, or to schedule a meeting outside of office hours.

The office hours aren't held when classes aren't in session (e.g., holidays and breaks).

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.

After submitting your application, you will receive a confirmation email with an "Edit Your Response" link. Save the email for your records. The link will allow you to make changes to your application if necessary.