Master of Science in Machine Learning — Curriculum

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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 analytical skills and a strong aptitude for mathematics, statistics and programming.

The program consists primarily of coursework, although students do have the chance to engage in research. Contact us with questions and concerns. 

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 degree in machine learning requires six core courses, three elective courses and a practicum.

M.S. 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 and Mathematical Statistics or 36-705: Intermediate Statistics

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

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 with an ML core faculty member
    • 10-620 Independent Study with an ML core faculty member
    • 10-620 Independent Study with an ML core faculty member

Notes

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.

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.

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.

  • 10-613/10-713: Machine Learning Ethics and Society (fall 2021, spring 2023)
  • 10-623: 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, 2022, 2023, 2024, 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, 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: 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)

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