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 DepartmentHuman-Computer Interaction InstituteInstitute for Software Research Language Technologies InstituteMachine 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 7 Core courses, 2 Elective courses, and a practicum.

Core

MS students take all seven Core courses:

  • 10-701 Introduction to Machine Learning or 10-715 Advanced Introduction to Machine Learning
  • 10-703 Deep Reinforcement Learning or 10-707 Topics in Deep Learning
  • 10-708 Probabilistic Graphical Models
  • 10-716 Advanced Machine Learning: Theory and Methods (formerly 10-702 Statistical Machine Learning)
  • 10-718 Data Analysis
  • 10-725 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 two Elective courses from separate lines:

    • 10-703 Deep Reinforcement Learning or 10-707 Topics in Deep Learning
    • 10-805 Machine Learning with Large Datasets (or 10-605 Machine Learning with Large Datasets taken in Fall 2020 or earlier)
    • 10-??? Special Topics in Machine Learning (course numbers vary)
    • 11-711 Algorithms for NLP
    • 11-741 Machine Learning for Text Mining
    • 11-747 Neural Networks for NLP
    • 11-777 Multimodal Machine Learning
    • 15-750 Algorithms
    • 15-780 Graduate Artificial Intelligence
    • 15-826 Multimedia Databases and Data Mining
    • 15-853 Algorithms in the Real World
    • 16-720 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

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

Note: A student may fulfill one or both Electives with Independent Study, if desired. This is generally done as one research project conducted over two semesters, since it takes time to get up to speed on a new research project, but it's possible to do research under different faculty in different semesters instead.

Examples of Special Topics Courses
  • 10-745 Scalability in Machine Learning (Fall 2019)

Practicum

MS students also complete a 36-unit practicum (an internship or research related to Machine Learning), generally conducted during the summer.