AI & Machine Learning

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

8 courses · 111 standalone talks · 283 total lectures

All AI & ML Language Models & NLP (114)Deep Learning (87)Statistical Learning & Causality (60)Algorithms & Data Structures (43)Reinforcement Learning (38)Complex Systems & Scaling (21)Neuroscience & Cognition (19)Probability & Information Theory (17)machine learning (15)Moral & Political Philosophy (14)

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Machine Learning (CS 229): Lecture 1 - Course Introduction

Machine Learning (CS 229): Lecture 1 - Course Introduction

AI & ML·Standalone Lecture · Andrew Ng · Stanford University

Andrew Ng Stanford University

1:08:40

From System 1 to System 2 Deep Learning

From System 1 to System 2 Deep Learning

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

Yoshua Bengio MILA, Université de Montréal

56:36

Hard Lefschetz Theorem and Hodge-Riemann Relations for Combinatorial Geometries

Hard Lefschetz Theorem and Hodge-Riemann Relations for Combinatorial Geometries

AI & ML·Research Seminar · June Huh · Institute for Advanced Study

June Huh Institute for Advanced Study

58:19

On the Measure of Intelligence: Program Synthesis and Fluid Abstraction

On the Measure of Intelligence: Program Synthesis and Fluid Abstraction

AI & ML·Public Colloquium · François Chollet · ARC Prize / Google

François Chollet ARC Prize / Google

1:10:44

An Observation on Generalization and Unsupervised Compression

An Observation on Generalization and Unsupervised Compression

AI & ML·Research Seminar · Ilya Sutskever · Simons Institute

Ilya Sutskever Simons Institute

57:21

What Needs to Be Added to Machine Learning? PAC Learning and Reasoning

What Needs to Be Added to Machine Learning? PAC Learning and Reasoning

Theory & Algos·Research Seminar · Leslie G. Valiant · Harvard University

Leslie G. Valiant Harvard University

54:36

A Collectivist, Economic Perspective on AI Systems

A Collectivist, Economic Perspective on AI Systems

AI & ML·Research Seminar · Michael I. Jordan · UC Berkeley

Michael I. Jordan UC Berkeley

54:44

The Mathematics of Causal Inference and the Logic of Science

The Mathematics of Causal Inference and the Logic of Science

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

Judea Pearl Simons Institute (UC Berkeley)

1:00:35

Lecture 18: Epilogue (CS 156)

Lecture 18: Epilogue (CS 156)

CS 156 · Lec 18 · Yaser Abu-Mostafa · Caltech

Yaser Abu-Mostafa Caltech

1:09:28

Lecture 16: Radial Basis Functions (CS 156)

Lecture 16: Radial Basis Functions (CS 156)

CS 156 · Lec 16 · Yaser Abu-Mostafa · Caltech

Yaser Abu-Mostafa Caltech

1:22:08

Lecture 15: Kernel Methods (CS 156)

Lecture 15: Kernel Methods (CS 156)

CS 156 · Lec 15 · Yaser Abu-Mostafa · Caltech

Yaser Abu-Mostafa Caltech

1:18:19

Lecture 12: Regularization (CS 156)

Lecture 12: Regularization (CS 156)

CS 156 · Lec 12 · Yaser Abu-Mostafa · Caltech

Yaser Abu-Mostafa Caltech

1:15:14

Lecture 11: Overfitting (CS 156)

Lecture 11: Overfitting (CS 156)

CS 156 · Lec 11 · Yaser Abu-Mostafa · Caltech

Yaser Abu-Mostafa Caltech

1:19:49

An Occam's Razor Principle for Transformers? (SIMONS-TCS-ML)

An Occam's Razor Principle for Transformers? (SIMONS-TCS-ML)

SIMONS-TCS-ML · Lec 16 · John Langford · UC Berkeley / Simons Institute

John Langford UC Berkeley / Simons Institute

39:55

Computational Thinking on Learning Models (SIMONS-TCS-ML)

Computational Thinking on Learning Models (SIMONS-TCS-ML)

SIMONS-TCS-ML · Lec 4 · Nati Srebro · UC Berkeley / Simons Institute

Nati Srebro UC Berkeley / Simons Institute

38:30

Liquid Vector Spaces and Condensed Mathematics

Liquid Vector Spaces and Condensed Mathematics

AI & ML·Research Seminar · Peter Scholze · Max Planck Institute for Mathematics

Peter Scholze Max Planck Institute for Mathematics

57:37

The Quantum Physicist as Causal Detective: Quantum Foundations and Causal Inference

The Quantum Physicist as Causal Detective: Quantum Foundations and Causal Inference

AI & ML·Public Colloquium · Robert Spekkens and Elie Wolfe · Perimeter Institute

Robert Spekkens and Elie Wolfe Perimeter Institute

1:04:06

Lecture 7: Neural Networks 1 (Architecture) (CS229)

Lecture 7: Neural Networks 1 (Architecture) (CS229)

AI & ML·Standalone Lecture · Chris Ré · Stanford University

Chris Ré Stanford University

1:20:28

Complexity of Lasso with Normalized Data

Complexity of Lasso with Normalized Data

Theory & Algos·Research Seminar · Vladimir Dragovich · University of Texas

Vladimir Dragovich University of Texas

59:53

Privacy Amplification for Correlated-Noise Mechanisms via b-Min-Sep Subsampling

Privacy Amplification for Correlated-Noise Mechanisms via b-Min-Sep Subsampling

Theory & Algos·Research Seminar · Andy Dong · Google TechTalks

Andy Dong Google TechTalks

57:54

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