Machine Learning, Minor
Dr. Matt Gormley, Program Director
Laura Winter, Program Coordinator
ml-minor@cs.cmu.edu
www.ml.cmu.edu/academics/minor-in-machine-learning.html
Machine Learning Minor
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 this field.
Curriculum
Prerequisites
Students must complete the prerequisites for 10-301 or 10-315 or 07-280. To view the prerequisites for 10-301 or 07-280, please click on the respective course. For each prerequisite, a grade of C or higher is required.
Core Courses (24 units)
Students must take two core courses, each being at least 12 credits
| Core Courses | Units | |
| 10-301 | Introduction to Machine Learning | 12 |
| or 10-315 | Introduction to Machine Learning (SCS Majors) | |
| or 07-280 | Artificial Intelligence and Machine Learning I | |
| 10-403 | Deep Reinforcement Learning & Control | 12 |
| or 10-405 | Machine Learning with Large Datasets (Undergraduate) | |
| or 10-414 | Deep Learning Systems: Algorithms and Implementation | |
| or 10-417 | Intermediate Deep Learning | |
| or 10-418 | Machine Learning for Structured Data | |
| or 10-422 | Foundations of Learning, Game Theory, and Their Connections | |
Electives (27 Units)
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.
Principal Courses
| 10-403 | Deep Reinforcement Learning & Control | 12 |
| or 10-703 | Deep Reinforcement Learning & Control | |
| 10-405 | Machine Learning with Large Datasets (Undergraduate) | 12 |
| or 10-605 | Machine Learning with Large Datasets | |
| or 10-745 | Scalability in Machine Learning | |
| 10-414 | Deep Learning Systems: Algorithms and Implementation | 12 |
| or 10-714 | Deep Learning Systems: Algorithms and Implementation | |
| 10-417 | Intermediate Deep Learning | 12 |
| or 11-485 | Introduction to Deep Learning | |
| or 10-707 | Advanced Deep Learning | |
| 10-418 | Machine Learning for Structured Data | 12 |
| or 10-618 | Machine Learning for Structured Data | |
| or 10-708 | Probabilistic Graphical Models | |
| 10-422 | Foundations of Learning, Game Theory, and Their Connections | 12 |
| 10-423 | Generative AI | 12 |
| or 10-623 | Generative AI | |
| 10-425 | Introduction to Convex Optimization | 12 |
| or 10-625 | Introduction to Convex Optimization | |
| or 10-725 | Optimization for Machine Learning | |
| 10-613 | Machine Learning Ethics and Society | 12 |
| 10-643 | Socio-technical Evaluations of Generative AI | 12 |
| or 10-743 | Socio-technical Evaluations of Generative AI | |
| 10-735 | Responsible AI | 12 |
| 10-777 | Historical Advances in Machine Learning | 12 |
| 36-401 | Modern Regression | 9 |
| 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.
Interdisciplinary Electives
| 02-510 | Computational Genomics | 12 |
| 03-511 | Computational Molecular Biology and Genomics | 9 |
| 10-335 | Art and Machine Learning | 12 |
| 11-411 | Natural Language Processing | 12 |
| 11-441 | Machine Learning with Graphs | 9 |
| 11-481 | Generative AI for Biomedicine | 12 |
| or 11-781 | Generative AI for Biomedicine | |
| 11-661 | Language and Statistics | 12 |
| 11-731 | Machine Translation and Sequence-to-Sequence Models | 12 |
| 11-751 | Speech Recognition and Understanding | 12 |
| 11-755 | Machine Learning for Signal Processing | 12 |
| 11-777 | Multimodal Machine Learning | 12 |
| 15-281 | Artificial Intelligence: Representation and Problem Solving | 12 |
| 15-386 | Neural Computation | 9 |
| 15-388 | Practical Data Science | 9 |
| 15-482 | Autonomous Agents | 12 |
| 16-311 | Introduction to Robotics | 12 |
| 16-385 | Computer Vision | 12 |
| 16-720 | Computer Vision | 12 |
| 16-745 | Optimal Control and Reinforcement Learning | |
| 16-824 | Visual Learning and Recognition | 12 |
| 16-831 | Introduction to Robot Learning | 12 |
| 17-537 | Artificial Intelligence Methods for Social Good | 9 |
| 36-402 | Advanced Methods for Data Analysis | 9 |
| 36-462 | Special Topics: Statistical Machine Learning | 9 |
| 36-463 | Special Topics: Multilevel and Hierarchical Models | 9 |
| 36-700 | Probability and Mathematical Statistics | 12 |
| Other courses as approved | ||
SCS Senior Honors Thesis
The SCS Senior Honors Thesis consists of 36 units of academic credit for this work. 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 SCS 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.
| 07-599 | SCS Honors Undergraduate Research Thesis | Var. |
Senior Research
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 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.
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.
| 10-500 | Senior Research Project | 24 |
Double Counting
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.)
Grades
All courses for the Machine Learning Minor, including prerequisites, must be passed with a C or better.
Admission
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. The application can be found on the Machine Learning Minor website.