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