Machine Learning

Course sites

• For technical questions: Please read/post publicly on Piazza
• For personal or administrative questions: Please post privately to All Instructors 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 here

Course Components

We will have weekly lectures (schedule details can be found here); 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.

Course Number Change

Since Fall 22, all MIT EECS subjects have been renumbered (rationale and details can be found here). This subject used to be called 6.867; moving forward, it will be 6.790, eventually. But for registration purposes, please register for 6.7900 (note the extra trailing zero) during this current transition phase.