Machine Learning

(draft site; unofficial)

Course Overview

• Graduate-level general introduction to machine learning; offered in fall semesters; 12 units (3-0-9)
• Prerequisites:
  • Linear algebra (at the level of 18.06 or 18.C06)
  • Probability (at the level of 6.3700, 6.3800, or 18.600)
• Recommended prerequisites: 6.3900, 6.C01, 6.S898, or other previous experience in machine learning.
• Brief description: Principles, techniques, and algorithms in machine learning from the point of view of statistical inference; representation, generalization, and model selection; and methods such as linear models, Bayesian networks, and deep learning models.
• The fall23 offering will have a relatively heavy focus on generative models and reinforcement learning; a tentative syllabus can be found here. For reference, fall22’s syllabus can be found here.

Course Components

We will have weekly lectures and recitations (schedule details can be found here); complementing those are homeworks, project, and exams. While some logistical details are TBC, the exams will be in lecture, in person, and scheduled before Monday, December 18, 2023 (i.e. we won’t have any exam during MIT’s final exam period).

Course Number Change

Since fall22, 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.