# 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 (10-702 will count if taken before Spring 2019)
- 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
- 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.