In Fall 2020 I gave the lectures for MIT's 6.036 course: Introduction to Machine Learning.
Below is a set of links to those lectures. Each link will be provided once captioning is completed for the corresponding video.
A playlist of all the videos available so far can be found at the following link: [youtube playlist].
Note: I will try to release errata and slides with corrections relative to the live lectures here, as necessary.
- Lecture 1: Basics (2020 / 09 / 01) [youtube] [slides pdf]
- Lecture 2: Perceptrons (2020 / 09 / 08) [youtube] [slides pdf]
- Lecture 3: Features (2020 / 09 / 15) [youtube] [slides pdf]
- Lecture 4: Logistic regression, a.k.a. linear logistic classification (2020 / 09 / 22) [youtube] [slides pdf]
- Lecture 5: Regression (2020 / 09 / 29) [youtube] [slides pdf]
- Lecture 6: Neural networks (2020 / 10 / 06) [youtube] [slides pdf]
- Lecture 7: There was a holiday this week, and the lecture numbering matches the week of the course. So there is no Lecture 7.
- Lecture 8: Convolutional neural networks (2020 / 10 / 20) [youtube] [slides pdf]
- Lecture 9: State machines and Markov decision processes (2020 / 10 / 27)
- Lecture 10: Reinforcement learning (2020 / 11 / 03)
- Lecture 11: Recurrent neural networks (2020 / 11 / 10)
- Lecture 12: Decision trees and random forests (2020 / 11 / 17)
- Lecture 13: Clustering (2020 / 12 / 01)
- Lecture 14: Guest lecture (David Sontag) (2020 / 12 / 08)
With thanks to:
- The lecture TAs Crystal Wang and Satvat Jagwani
- The 6.036 Fall 2020 course staff (instructors, TAs, and LAs) and past 6.036 course staff
- Jeffrey Shen for captioning
- The MIT EECS Department for funding captioning
- Duane Boning, Isaac Chuang, and Leslie Kaelbling for support in posting these videos
If you find any ways to improve how well the video captions reflect the live lectures, please submit a pull request to