Master of Science in Machine Learning Curriculum

The MS 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.

Curriculum

The curriculum for the Master's in Machine Learning requires 4 Set Core courses, 3 Menu Core courses, 2 Electives, and a practicum.

Set Core

MS students take all four Set Core courses:

  • 10-701 Introduction to Machine Learning or 10-715 Advanced Introduction to Machine Learning
  • 10-716 Advanced Machine Learning: Theory and Methods (formerly 10-702 Statistical Machine Learning)
  • 10-718 Data Analysis
  • 36-700 Probability & Mathematical Statistics or 36-705 Intermediate Statistics

Menu Core

Students take their choice of three Menu Core courses from separate lines:

  • 10-703 Deep Reinforcement Learning or 10-707 Topics in Deep Learning
  • 10-708 Probabilistic Graphical Models
  • 10-725 Convex Optimization
  • 10-620 Independent Study or 10-940 Independent Study (under ML Core Faculty or under the student's ML PhD advisor if in the ML PhD)

Note: The three Menu Core courses must be taken from three 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 Menu Core requirements.

Electives

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

    • 10-703 Deep Reinforcement Learning
    • 10-707 Topics in Deep Learning
    • 10-708 Probabilistic Graphical Models
    • 10-725 Convex Optimization
    • 10-805 Machine Learning with Large Datasets (or 10-605 Machine Learning with Large Datasets taken in Spring 2019 or earlier)
    • 10-??? Special Topics in Machine Learning (course numbers vary)
    • 15-750 Algorithms
    • 15-780 Graduate Artificial Intelligence
    • 15-826 Multimedia Databases and Data Mining
    • 15-853 Algorithms in the Real World
    • 36-707 Regression Analysis
    • 36-709 Advanced Probability
    • 36-710 Advanced Statistical Theory
    • 10-620 Independent Study or 10-940 Independent Study (under ML Core Faculty or, if in the ML PhD, under the ML PhD advisor)

Note: If a student takes 10-703 Deep Reinforcement Learning and 10-707 Topics in Deep Learning, one will count for the Menu Core and the other will count as an Elective. If a student takes Independent Study twice, one will count for the Menu Core and the other will count as an Elective. (12 units of Independent Study = 1 course.)

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.