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Machine Learning Core Courses

The Machine Learning Core Courses for the graduate programs consists of 6 courses.

Set Core:

These 4 required core courses together provide a foundation in machine learning, statistics, probability, and algorithms:

10-701 Introduction to Machine Learning or 10-715 Advanced Introduction to Machine Learning*
10-716 Advanced Machine Learning: Theory and Methods
36-700 Probability & Mathematical Statistics or 36-705 Intermediate Statistics*
10-718 Machine Learning in Practice

*Note: MS students may take 10-701 Introduction to Machine Learning & 36-700 Probability & Mathematical Statistics. PhD students must take 10-715 Advanced Introduction to Machine Learning & 36-705 Intermediate Statistics.

Plus any 2 of the fellow Menu Core courses:

10-703 Deep Reinforcement Learning or 10-707 Topics in Deep Learning
10-708 Probabilistic Graphical Models
10-725 Convex Optimization
10-734 Foundations of Autonomous Decision Making under Uncertainty
15-750 Algorithms in the Real World or 15-850 Advanced Algorithms
15-780 Graduate Artificial Intelligence
10-805 Machine Learning with Large Datasets
36-707 Regression Analysis
36-709 Advanced Statistical Theory I
36-710 Advanced Statistical Theory II

Note: The two Menu Core courses must be taken from separate lines. E.g., a student may not use both 15-750 Algorithms in the Real World and 15-850 Advanced Algorithms to satisfy their Menu Core requiements. Menu Core courses may also be used as electives.