6.390 IntroML (Spring26) - Lecture 4 Linear Classification

6.390 IntroML (Spring 26) - Lecture 3 Gradient Descent Methods

6.390 IntroML (Spring26) - Lecture 2 Regularization and Cross-validation

6.390 IntroML (Spring26) - Lecture 1 Intro and Linear Regression

expanding-horizons-talk

6.390 IntroML (Fall25) - Lecture 12 Reinforcement Learning

6.390 IntroML (Fall25) - Lecture 11 Markov Decision Processes

6.390 IntroML (Fall25) - Lecture 10 Non-parametric Models

6.390 IntroML (Fall25) - Lecture 9 Transformers

6.390 IntroML (Fall25) - Lecture 8 Representation Learning

IntroML (Fall25) - Lecture 7 Convolutional Neural Networks

6.390 IntroML (Fall25) - Lecture 5 Features, Neural Networks I

6.390 IntroML (Fall25) - Lecture 4 Linear Classification

6.390 IntroML (Fall25) - Lecture 3 Gradient Descent Methods

6.390 IntroML (Fall25) - Lecture 2 Regularization and Cross-validation

6.390 IntroML (Fall 25) - Lecture 1 Intro and Linear Regression

6.C011/C511 - Course Overview

Robotics and Generative AI

MIT Math Undergraduate Association Talk Series

6.C011/C511 - ML for CS (Spring25) - Lecture 9 EECS Case Studies & Integration

6.C011/C511 - ML for CS (Spring25) - Lecture 8 Domain Shift, Adaptation, and Robustness