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.
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 (formerly 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 three Elective courses from separate lines:
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- 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 (formerly 10-702 Statistical Machine Learning)
- 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 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 Topics in 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 Program Coordinator, Dorothy Holland-Minkley.
Examples of Special Topics Courses
- 10-730 Advanced AI and Brain Seminar (Spring 2021; 6 units = 1/2 Elective)
- 10-745 Scalability in Machine Learning (Fall 2019, Spring 2022)
- 10-613/10-713 Machine Learning Ethics and Society (Fall 2021, Spring 2023)
- 10-714 Deep Learning Systems: Algorithms and Implementation (Fall 2021, Fall 2022, Fall 2023)
- 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)
- 10-732 Robustness and Adaptation in Shifting Environments (Fall 2022)
- 10-777 Historical Advances in Machine Learning (Fall 2021)
- 10-813 Advanced Topics in Machine Learning Theory (Fall 2022)
Practicum
MS students also complete a one-semester, full-time practicum (an internship or research related to machine learning), generally conducted during the summer.