Carnegie Mellon University School of Computer Science

Source: https://ml.cmu.edu/academics/ml-concentration Parent: https://ai.cmu.edu/curriculum

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Academics Undergraduate Concentration 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 Concentration in Machine Learning allows undergraduates to learn about the core principles of this field. The concentration requires five courses (two core courses and three electives) from the School of Computer Science (SCS) and the Department of Statistics and Data Science. The electives primarily focus on core machine learning skills that could be broadly applicable to either industry or graduate work. A CS Senior Honors Thesis or two semesters of Senior Research may be used to satisfy part of the elective requirement, which could provide excellent research experience for students interested in pursuing a Ph.D.

Learning Objectives

Upon completion of this concentration, students should be able to:

• Formulate real-world problems involving data such that they can be solved by machine learning.
• Implement and analyze existing learning algorithms.
• Employ probability, statistics, calculus, linear algebra and optimization in order to develop new predictive models or learning methods.
• Select and apply an appropriate supervised learning algorithm for problems of different kinds, including classification, regression, structured prediction, clustering and representation learning.
• Describe the the formal properties of models and algorithms for learning and explain the practical implications of those results.
• Compare and contrast different paradigms for learning.

Eligibility

The School of Computer Science offers concentrations for SCS students in various aspects of computing to provide greater depth to their education. Information can be found in the Undergraduate Course Catalog. Students outside SCS are not eligible for the Machine Learning Concentration and should instead consider the Machine Learning Minor.

• Email ML Concentration Director
• Apply for the ML Concentration

Concentration Requirements

Prerequisites

• CS background — 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.

Double-Counting Restrictions

At least three courses (each being at least nine units) must be used for only the Machine Learning Concentration, 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 only be used for the Machine Learning Concentration.)

Curriculum

Core — Two Courses

Students must take two core courses, each being at least nine units:

• 10-301 or 10-315: Introduction to Machine Learning, or 07-280: Artificial Intelligence and Machine Learning I

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

Electives — Three Courses

Students need to take three courses from the following list, each being at least nine units. Students may substitute one of these courses with one semester of an SCS Senior Honors Thesis or equivalent senior research credit.

• 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: Topics in Deep Learning
• 10-418: 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
• 10-425/10-625: Introduction to Convex Optimization or10-725: 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

Important Notes

• To avoid excessive overlap in covered material, at most one of the core deep learning courses may be used to fulfill concentration course requirements: 10-417, 10-617, 11-485 or 10-707. In general, students are discouraged from taking more than one of these.
• 15-281: Artificial Intelligence covers several topics (i.e., reinforcement learning and Bayesian networks) that are complementary to 10-315. While not part of the ML Concentration curriculum, this course is also one to consider.
• 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 concentration, if the student is adequately prepared for the more advanced version and the home department approves the student's registration.
• Be aware that not all graduate-level courses in the Machine Learning Department may be used as electives. In particular, 10-606/10-607: Computational Foundations for Machine Learning may not be used as electives for the ML concentration.

SCS Senior Honors Thesis

The SCS Senior Honors Thesis consists of 36 units of academic credit. Up to 12 units may be counted toward the ML concentration. Students must consult with the Computer Science Department for information about the SCS 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

Senior research consists of two semesters of 10-500: Senior Research Project, totaling 24 units. Up to 12 units may be counted toward the ML concentration.

The research must be a yearlong senior project, supervised or co-supervised by a machine learning core faculty member. It is almost always conducted as two semester long projects, and must be done in the 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 Concentration Director to confirm that the project will count for the Machine Learning Concentration.

Your one-page research proposal should contain the following:

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

The student should email the ML Concentration 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 Concentration Director at the end of their senior year.

Students are encouraged to reach out to the Concentration 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-concentration@cs.cmu.edu with questions about about eligibility, curriculum and more.

Matt Gormley is holding office hours Spring 2026 as follows:

Friday, April 10, 2:00 - 2:30 pm, GHC 8103

Friday April 17, 2:00 - 2:30 pm, GHC 8103

Laura Winter holds office hours during Spring and Fall.  Spring office hours are being held on Mondays, 10 - 11 am, in GHC 9112.  You can also email Laura 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

The Machine Learning Concentration is only open to students with SCS majors. Students can apply beginning in sophomore year, after they have completed the prerequisites, and are encouraged to apply at least one semester before graduating.

To apply, complete the Machine Learning Concentration 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 Concentration (which can be changed later), and a brief (150-250 word) Statement of Purpose describing your reasons for pursuing the ML Concentration. 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.