Machine Learning Core Courses
The Machine Learning Core Courses for the graduate programs consists of 6 courses.
These 3 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 (10-702 will count if taken before Spring 2019)
- 36-700 Probability & Mathematical Statistics or 36-705 Intermediate Statistics*
*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
- 15-750 Algorithms or 15-853 Algorithms in the Real World
- 15-780 Graduate Artificial Intelligence
- 15-826 Multimedia Databases and Data Mining or 10-805 Machine Learning with Large Datasets
- 36-707 Regression Analysis
- 36-709 Advanced Statistical Theory I
Note: The two Menu Core courses must be taken from separate lines. E.g., a student may not use both 15-750 Algorithms and 15-853 Algorithms in the Real World to satisfy their Menu Core requirements. Menu Core courses may also be used as electives.
Plus the Data Analysis Course (DAC):
ML students are required to take the Data Analysis Course which focuses on applying machine learning techniques to real-world data, letting students explore how to use incomplete and imperfect data sets to gain useful results.