Carnegie Mellon University

Master of Science in Machine Learning Curriculum

The Master of Science in Machine Learning offers students with a Bachelor's degree the opportunity to improve their training with advanced study in Machine Learning. Incoming students should have good analytic skills and a strong aptitude for mathematics, statistics, and programming.

The program consists primarily of coursework, although students do have the opportunity to engage in research. For questions and concerns, please contact us. 

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 machine learning.

The curriculum varies based on when students began their undergraduate program at CMU:

Curriculum for 2018 and earlier
Curriculum for 2019 and later

Machine Learning Concentration

Students within the School of Computer Science can add the Machine Learning Concentration to their major to enhance their computer science education.

Statistics & Machine Learning Major

This joint major, managed by the Dietrich College of Humanities and Social Sciences, develops the critical ideas and skills underlying statistical machine learning — the creation and study of algorithms that enable systems to automatically learn and improve with experience. It is ideal for students interested in statistical computation, data science, or "Big Data" problems, including those planning to pursue a related PhD or a job in the tech industry.

Bachelor's of Science in Artificial Intelligence

Carnegie Mellon has led the world in artificial intelligence education and innovation since the field was created. It's only natural, then, that the School of Computer Science would offer the nation's first bachelor's degree in artificial intelligence, which we introduced in fall 2018. A B.S. in AI from Carnegie Mellon University, unites disciplines from machine learning to natural language processing, instruction in the BSAI program includes faculty members from the school's Computer Science Department, Human-Computer Interaction Institute, Institute for Software Research,  Language Technologies Institute, Machine Learning Department and Robotics Institute.

Courses in Machine Learning

These courses are being offered by the Machine Learning Department this semester.

Teaching Assistantships

Apply to be a Teaching Assistant or Course Assistant in the Machine Learning Department. Both graduate and undergraduate students are welcome to apply.

Curriculum

The curriculum for the Master's in Machine Learning requires 6 Core courses, 3 Elective courses, and a practicum.

Core

MS students take all six Core courses:

  • 10-701 Introduction to Machine Learning or 10-715 Advanced Introduction to Machine Learning
  • 10-617 Intermediate Deep Learning or 10-703 Deep Reinforcement Learning or 10-707 Advanced Deep Learning
  • 10-708 Probabilistic Graphical Models
  • 10-718 Machine Learning in Practice
  • 10-725 Optimization for Machine Learning (formerly Convex Optimization)
  • 36-700 Probability & Mathematical Statistics or 36-705 Intermediate Statistics

Note: The Core courses must be taken from separate lines. E.g., a student may not use both 10-703 Deep Reinforcement Learning and 10-707 Topics in Deep Learning to satisfy their Core requirements.

Electives

Students take their choice of three Elective courses from separate lines:

    • 10-605/10-805 Machine Learning with Large Datasets
    • 10-703 Deep Reinforcement Learning or 10-707 Advanced Deep Learning
    • 10-716 Advanced Machine Learning: Theory and Methods
    • 10-??? Special Topics in Machine Learning (course numbers vary)
    • 11-711 Advanced Natural Language Processing
    • 11-741 Machine Learning with Graphs
    • 11-747 Neural Networks for NLP
    • 11-777 Multimodal Machine Learning
    • 15-750 Algorithms in the Real World or 15-850 Advanced Algorithms or 15-853 Algorithms in the Real World
    • 15-780 Graduate Artificial Intelligence
    • 15-826 Multimedia Databases and Data Mining
    • 16-720 Computer Vision or 16-820 Advanced Computer Vision
    • 36-707 Regression Analysis
    • 36-709 Advanced Statistical Theory I
    • 36-710 Advanced Statistical Theory II
    • 10-620 Independent Study, under ML Core Faculty
    • 10-620 Independent Study, under ML Core Faculty
    • 10-620 Independent Study, under ML Core Faculty

Note: If a student takes both 10-703 Deep Reinforcement Learning and 10-707 Advanced Deep Learning, one will count for the Core and the other will count as an Elective.

Note: A student may fulfill one, two, or three Electives with Independent Study, if desired. The most common arrangement is one research project conducted over two semesters (counting as two Electives), since it takes time to get up to speed on a new research project, but a project may be as short as one semester or as long as three semesters plus the summer practicum. Depending on the project(s), it's possible to do research under different faculty in different semesters, but only one Independent Study can be completed at a time.

Note: Multiple Special Topics in Machine Learning courses can be used as Electives; it is not limited to one Special Topics course per student. These courses will generally have 10-XXX course numbers, but not all 10-XXX courses are approved as Electives. To know if a specific course counts as an Elective, consult the list below or email the MSML Programs Manager, Dorothy Holland-Minkley.

Examples of Special Topics Courses
  • 10-613/10-713 Machine Learning Ethics and Society (Fall 2021, Spring 2023)
  • 10-623/-723 Generative AI (Spring 2024, Fall 2024, Spring 2025, Fall 2025)
  • 10-624 Bayesian Methods in Machine Learning (Spring 2025)
  • 10-643/-743 Socio-technical Evaluations of Generative AI (Fall 2025)
  • 10-714 Deep Learning Systems: Algorithms and Implementation (Fall 2021, Fall 2022, Fall 2023, Fall 2024, Fall 2025)
  • 10-717 The Art of the Paper (Spring 2022; 6 units = 1/2 Elective)
  • 10-719 Federated and Collaborative Learning (Fall 2023)
  • 10-721 Philosophical Foundations of Machine Intelligence (Fall 2021; 6 units = 1/2 Elective)
  • 10-726 Machine Learning Theory: The Art of Problem Formulation and Theorem Proving (Fall 2025)
  • 10-730 Advanced AI and Brain Seminar (Spring 2021; 6 units = 1/2 Elective)
  • 10-732 Robustness and Adaptation in Shifting Environments (Fall 2022)
  • 10-733 Representation and Generation in Neuroscience and AI (Spring 2024, Spring 2025)
  • 10-734 Foundations of Autonomous Decision Making under Uncertainty (Fall 2024, Fall 2025)
  • 10-735 Responsible AI (Spring 2024, Fall 2024); note that 80-831 does not count
  • 10-736 Human-AI Decision Complementarity for Decision-Making (Spring 2025)
  • 10-741 Representation Learning (Fall 2024)
  • 10-742 Machine Learning in Healthcare (Fall 2024)
  • 10-744 Machine Learning for Science (Fall 2025)
  • 10-745 Scalability in Machine Learning (Fall 2019, Spring 2022)
  • 10-747 Neuro-Symbolic AI (Fall 2025)
  • 10-748 Machine Learning in Epidemiology (Fall 2025)
  • 10-777 Historical Advances in Machine Learning (Fall 2021) 
  • 10-799 Special Topics: Data Privacy, Memorization, and Copyright in Generative AI (Fall 2024; 6 units = 1/2 Elective)
  • 10-813 Advanced Topics in Machine Learning Theory (Fall 2022)
  • 10-880 Game Theoretic Probability, Statistics and Learning (Spring 2024)
  • 17-716 AI Governance: Identifying and Mitigating Risks in the Design and Development of AI Solutions (Spring 2024, Spring 2025; 6 units = 1/2 Elective)

Practicum

MS students also complete a one-semester, full-time practicum (an internship or research related to machine learning), generally conducted during the summer.