Machine Learning

Course sites

• For personal or administrative questions, please email the course staff email 6790-fa26@mit.edu.
• For technical questions, please read/post on Piazza.
• For grades on submitted work, please check Canvas.

Course Overview

• Probabilistic foundations of machine learning; offered in fall semesters; 12 units (3-0-9)
• Prerequisites:
  • Introductory machine learning (at the level of 6.390 or 6.370 or 6.C01)
  • Linear algebra (at the level of 18.06 or 18.C06)
  • Probability (at the level of 6.3700, 6.3800, or 18.600)
• Brief description: Probabilistic thinking is critical to understanding machine learning, in techniques ranging from classic linear models to modern deep networks. We will study model representation, generalization, learning algorithms, and model-selection with mathematical rigor as well as an emphasis on how to apply these methods in applications with real-world consequence.
• A syllabus can be found on Piazza.

Course Components

We will have weekly lectures; complementing those are written homework, small projects, a midterm, and a final exam.

Homeworks, TeX templates for homework submissions, projects, and lecture notes will be posted on Piazza.