Academic Discipline

AI & Machine Learning

Deep learning, language models, reinforcement learning, statistical learning theory, representation learning, and vision.

10courses86standalone talks213total lectures

Subfields

All AI & ML213 Deep Learning80 Language Models & NLP73 Reinforcement Learning35 Statistical Learning & Causality35 Computer Vision & Graphics28 Mechanistic Interpretability, Alignment & World Models27 Active Inference, Cybernetics & Artificial Life2

Statistical Learning & Causality— Generalization bounds, VC dimension, kernel methods, Bayesian inference, and causal inference.

3 topics

All Statistical Learning & Causality Statistical Learning & Generalization21 Bayesian & Causal Inference16 Model Complexity2

Bayesian & Causal Inference · Statistical Learning & Causality

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Learning Representations: A Challenge for Learning Theory, COLT 2013

AI & ML·Conference Talk·Statistical Learning & Causality · Yann LeCun · New York University

Yann LeCun

New York University

54:33

Neural Networks I: Feedforward Architectures & Backpropagation

AI & ML·Standalone Lecture·Statistical Learning & Causality · Chris Ré · Stanford University

Chris Ré

Stanford University

1:20:28

The Learning Problem

CS 156 · Learning From Data · Lec 1·Statistical Learning & Causality · Yaser Abu-Mostafa · Caltech

Yaser Abu-Mostafa

Caltech

1:21:27

From System 1 to System 2 Deep Learning

AI & ML·Conference Talk·Language Models & NLP · Yoshua Bengio · MILA, Université de Montréal

Yoshua Bengio

MILA, Université de Montréal

56:36

An Observation on Generalization and Unsupervised Compression

AI & ML·Research Seminar·Language Models & NLP · Ilya Sutskever · UC Berkeley

Ilya Sutskever

UC Berkeley

57:21

Active Inference and the Free Energy Principle of Brain Function

AI & ML·Public Colloquium·Neuroscience & Cognition · Karl Friston · University College London

Karl Friston

University College London

24:17

The Mathematics of Causal Inference and the Logic of Science

AI & ML·Research Seminar·Bayesian & Causal Inference · Judea Pearl · Simons Institute (UC Berkeley)

Judea Pearl

Simons Institute (UC Berkeley)

1:00:35

On the Measure of Intelligence: Program Synthesis and Fluid Abstraction

AI & ML·Public Colloquium·Statistical Learning & Causality · François Chollet · Google Research & TechTalks

François Chollet

Google Research & TechTalks

1:10:44

The Mathematics of AI Uncertainty

AI & ML·Public Colloquium·Statistical Learning & Causality · Zoubin Ghahramani · University of Cambridge

Zoubin Ghahramani

University of Cambridge

44:42

What Really Causes Recessions?

AI & ML·Public Colloquium·Bayesian & Causal Inference · Tyler Goodspeed · University of Austin (UATX)

Tyler Goodspeed

University of Austin (UATX)

1:15:37

Complexity of Lasso with Normalized Data

AI & ML·Research Seminar·Computational Complexity · Vladimir Dragovich · UT Austin

Vladimir Dragovich

UT Austin

59:53

Building makemore Part 2: Multi-Layer Perceptron Language Models (Bengio et al. 2003)

Neural Networks: Zero to Hero · Lec 3·Statistical Learning & Causality · Andrej Karpathy · Independent / Eureka Labs

Andrej Karpathy

Independent / Eureka Labs

1:15:39

Is Learning Feasible?

CS 156 · Learning From Data · Lec 2·Statistical Learning & Generalization · Yaser Abu-Mostafa · Caltech

Yaser Abu-Mostafa

Caltech

1:16:48

Loss Functions and Optimization

Convolutional Neural Networks for Visual Recognition · Lec 3·Deep Learning · Fei-Fei Li, Justin Johnson & Serena Yeung · Stanford University

Fei-Fei Li&Justin Johnson

Stanford University

1:14:40

Generalization Theory

6.7960 · Deep Learning · Lec 6·Deep Learning · Phillip Isola · MIT

Phillip Isola

MIT