6.390 IntroML Spring25 Final Review

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

6.C011/C511 - ML for CS (Spring25) - Lecture 7 - Reinforcement Learning III (Actor-critic; variance reduction)

6.C011/C511 - ML for CS (Spring25) - Lecture 6 Reinforcement Learning II (Value, Policy Gradient)

6.C011/C511 - ML for CS (Spring25) - Lecture 5 Reinforcement Learning I (Value-based methods)

6.C011/C511 - ML for CS (Spring25) - Lecture 4 Generative Models - Scaling Up

6.C011/C511 - ML for CS (Spring25) - Lecture 3 Generative Models - Diffusion

6.C011/C511 - ML for CS (Spring25) - Lecture 2 Generative Models - Autoregressive

6.C011/C511 - ML for CS (Spring25) - Lecture 1 Representation Learning

6.390 IntroML (Spring25) - Lecture 12 Non-parametric Models

Robotics and Generative AI

HackMIT Blueprint

Robotics and Generative AI

talk at Harvard-MIT Mathematics Tournament (HMMT)

6.390 IntroML (Spring25) - Lecture 11 Reinforcement Learning

6.390 IntroML (Spring25) - Lecture 10 Markov Decision Processes

6.390 IntroML (Spring25) - Lecture 9 Transformers

6.390 IntroML (Spring25) - Lecture 8 Representation Learning

IntroML (Spring25) - Lecture 7 Convolutional Neural Networks

6.390 IntroML (Spring25) - Lecture 6 Neural Networks II

6.390 IntroML (Spring25) - Lecture 5 Features, Neural Networks I

6.390 IntroML (Spring25) - Lecture 4 Linear Classification

6.390 IntroML (Spring25) - Lecture 3 Gradient Descent Methods

6.390 IntroML (Spring25) - Lecture 2 Linear Regression and Regularization

6.390 IntroML (Fall24) - Lecture 13 Non-parametric Models

6.390 IntroML (Fall24) - Lecture 12 Reinforcement Learning

6.390 IntroML (Fall24) - Lecture 11 Markov Decision Processes

6.390 IntroML (Fall24) - Lecture 10 Clustering

6.390 IntroML (Fall24) - Lecture 9 Transformers

6.390 IntroML (Fall24) - Lecture 8 Convolutional Neural Networks

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6.390 IntroML (Fall24) - Lecture 7 Auto-encoders (Representation Learning)

6.390 IntroML (Fall24) - Lecture 6 Neural Networks

6.390 IntroML (Fall24) - Lecture 5 Features

6.390 IntroML (Fall24) - Lecture 4 Linear Classification

6.390 IntroML (Fall24) - Lecture 3 Gradient Descent Methods

6.390 IntroML (Fall24) - Lecture 2 Linear Regression and Regularization

Guest Lecture - Some recent ML trends/applications

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