Undergraduate Minor in Machine Learning

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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 the field.

Eligibility

The Machine Learning Minor is open to undergraduate students in any major at Carnegie Mellon University 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

All courses for the ML minor, including prerequisites, must be passed with a C or better.

  • CS background — 15-121: Introduction to Data Structures; or 15-122: Principles of Imperative Computation.
  • Math background — 15-151: Mathematical Foundations for Computer Science; 21-127: Concepts of Mathematics; or 21-128: Mathematical Concepts and Proofs.
  • Probability & statistics background — 36-218: Probability Theory for Computer Scientists; 36-219: Probability Theory and Random Processes; 36-225: Introduction to Probability Theory; 36-235: Probability and Statistical Inference I; 15-259: Probability and Computing, or 21-325: Probability.

No course in the machine learning minor may be counted toward another SCS minor. Additionally, at least three courses (each being at least nine 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 toward other majors, minors and concentrations and do not count toward the three courses that must be used for only the machine learning minor.)

Curriculum

Students pursuing a machine learning minor take two core courses that provide a foundation in the field. They include:

  • 10-301 or 10-315: Introduction to Machine Learning
And one of the following courses:
  • 10-403: Deep Reinforcement Learning and 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 three elective courses of at least nine units each in machine learning. Students may select one of the following options to satisfy the electives requirement:

  • Three principal courses.
  • Two principal courses + one interdisciplinary course.
  • Two principal courses + one semester of CS Senior Honors Thesis or Senior Research.
  • One principal course + two 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.

The following principle courses are acceptable options for fulfilling the elective requirement.
  • 10-403/10-703: Deep Reinforcement Learning and 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
  • 10-643/10-743/11-805 Socio-technical Evaluations of Generative AI
  • 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 in the list above. For example, if 10-417: Intermediate Deep Learning is used for the ML minor, 11-485: Introduction to Deep Learning cannot 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
  • 11-481/781 Generative AI for Biomedicine
  • 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 in the list above. For example, if 36-700: Probability and Mathematical Statistics is used for the ML minor, 36-705: Intermediate Statistics cannot be used for the ML minor.

The CS Senior Honors Thesis consists of 36 units of academic credit, usually under the course number 07-599: SCS Honors Undergraduate Research Thesis. Up to 24 units (12 units each semester) may be counted toward the ML minor. Students must consult with the Computer Science Department for information about the CS Senior Honors Thesis. Once both student and adviser 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 two semesters of 10-500: Senior Research Project, totaling 24 units and counting as two electives.

The research must be a yearlong senior project, supervised or co-supervised by a machine learning core faculty or affiliated faculty member. It is almost always conducted as two semesterlong 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 adviser 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 adviser's name.
  • The following seven sections, which you should bold in your proposal. 
    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
  • Your research adviser's signature, 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 their 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 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. Contact them about eligibility, curriculum and other relevant questions.

Office hours for both Matt Gormley and Laura Winter will be announced before the fall 2025 semester. Note that office hours are only held when classes are in session (i.e., there are no office hours on holidays or 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.