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Stanford CME295 Transformers & LLMs | Autumn 2026 | Lecture 1 - Transformers

AI in Healthcare Series: Have We Already Bent the Healthcare Cost Curve?

Stanford Webinar - A Conversation on the Future of Translational Medicine

AI in Healthcare Series: When AI Stops Being a Project

Stanford AA203 Optimal and Learning-Based Control | Spring 2026 | Lecture 19: Model-Based RL

Stanford AA203 Optimal and Learning-Based Control | Spring 2026 | Lecture 18: RL Policy Optimization

Stanford AA203 Optimal and Learning-Based Control | Spring 2026 | Lecture 17: RL Value-Based Methods

Stanford AA203 Optimal and Learning-Based Control | Spring 2026 | Lecture 16: Fundamentals of RL

Stanford AA203 Optimal and Learning-Based Control | Spring 2026 | Lecture 15: Imitation Learning

Stanford AA203 Optimal and Learning-Based Control | Spring 2026 | Lecture 14: Intro to IL and RL

Stanford AA203 Optimal and Learning-Based Control | Spring 2026 | Lecture 13: Intro to Learning

Stanford AA203 Optimal and Learning-Based Control | Spring 2026 | Lecture 12: Feasibility of MPC

Stanford AA203 Optimal and Learning-Based Control | Spring 2026 | Lecture 11: Introduction to MPC

Stanford AA203 Optimal and Learning-Based Control | Spring 2026 | Lecture 10: Reachibility Analysis

Stanford AA203 Optimal and Learning-Based Control | Spring 2026 | Lecture 9: Stochastic Dyn. Program

AStanford AA203 Optimal and Learning-Based Control | Spring 2026 | Lecture 8: LQR-Style Algorithms

Stanford AA203 Optimal and Learning-Based Control | Spring 2026 | Lecture 7: Dynamic Programming

Stanford AA203 Optimal and Learning-Based Control | Spring 2026 | Lecture 6: Direct Methods

Stanford AA203 Optimal and Learning-Based Control | Spring 2026 | Lecture 5: Computational Methods

Stanford AA203 Optimal and Learning-Based Control | Spring 2026 | Lecture 4: Indirect Methods

Stanford AA203 Optimal and Learning-Based Control | Spring 2026 | Lecture 3: Calculus of Variations

Stanford AA203 Optimal and Learning-Based Control | Spring 2026 | Lecture 2: Optimization Theory

Stanford AA203 Optimal and Learning-Based Control | Spring 2026 | Lecture 1: Course Overview

Stanford CS329A Self-Improving AI Agents | Part 4 | Learning from Feedback with Tools/Code

Stanford CS329A Self-Improving AI Agents | Part 1 | Course Overview

Stanford CS329A Self-Improving AI Agents | Part 2 | Test-Time Compute Scaling

Stanford CS329A Self-Improving AI Agents | Part 3 | Robust Verification

Stanford CS329A Self-Improving AI Agents | Part 6 | Train Time Scaling/Scaling RL

Stanford CS329A Self-Improving AI Agents | Part 7 | Self-Improvement and Deep Research Agents

Stanford CS329A Self-Improving AI Agents | Part 5 | Planning and Multi-Step Reasoning

Stanford CS329A Self-Improving AI Agents | Part 9 | Future Research Areas

Stanford CS329A Self-Improving AI Agents | Part 8 | Agentic Evaluations and Long Horizon Tasks

Stanford CS547 HCI Seminar | Spring 2026 | Show It or Tell It? Text, Visualization, and Combination

Stanford CS229 Machine Learning | Spring 2026 | Lecture 20: GMM (EM), PCA

Stanford CS229 Machine Learning | Spring 2026 | Lecture 18: GMM (EM), PCA

Stanford CS229 Machine Learning | Spring 2026 | Lecture 16: Basic Concept in RL, Policy Gradient

Stanford CS229 Machine Learning | Spring 2026 | Lecture 14: Transformers, In-Context Learning

Stanford CS229 Machine Learning | Spring 2026 | Lecture 13: LLMs, Next-Word Prediction Loss

Stanford CS229 Machine Learning | Spring 2026 | Lecture 12: Representation Learning

Stanford CS229 Machine Learning | Spring 2026 | Lecture 11: Diffusion Models

Stanford CS229 Machine Learning | Spring 2026 | Lecture 10: GMM (EM), PCA

Stanford CS229 Machine Learning | Spring 2026 | Lecture 9: K-Means and GMM (non-EM)

Stanford CS229 Machine Learning | Spring 2026 | Lecture 8: Neural Networks 2 (Backprop)

Stanford CS229 Machine Learning | Spring 2026 | Lecture 7: Neural Networks 1 (Architecture)

Stanford CS229 Machine Learning | Spring 2026 | Lecture 6: Dataset Split, ML Advice

Stanford CS229 Machine Learning | Spring 2026 | Lecture 5: Gaussian Discriminant Analysis

Stanford CS229 Machine Learning | Spring 2026 | Lecture 4: Exponential Family, GLMs Classification

Stanford CS229 Machine Learning | Spring 2026 | Lecture 3: Weighted Least Squares

Stanford CS229 Machine Learning | Spring 2026 | Lecture 2: Supervised Learning Setup

Stanford CS229 Machine Learning | Spring 2026 | Lecture 1: Introduction

Stanford MS&E435 Economics of the AI Supercycle | Spring 2026 | The GPU Economy

Stanford Robotics Seminar ENGR319 | Winter 2025 | Embodied Intelligence

Stanford CS547 HCI Seminar | Spring 2026 | Promoting Agency in Human-AI Interaction

Stanford MS&E435 Economics of the AI Supercycle | Spring 2026 | Economics of Generative AI

Stanford MS&E435 Economics of the AI Supercycle | Spring 2026 | Applications, AI in Life Sciences

Stanford CS547 HCI Seminar | Spring 2026 | Just-in-Time Objectives for Specialized AI Interactions

Stanford CS547 HCI Seminar | Spring 2026 | Toward Ontological Multiplicity in AI and Computing

Stanford MS&E435 Economics of the AI Supercycle | Spring 2026 | Infrasctructure, Enterprise AI, SaaS

Live from Stanford Health AI Week

Stanford Robotics Seminar ENGR319 | Spring 2026 | Towards Trustworthy Autonomy

Stanford CS153 Frontier Systems | Building the Frontier Ecosystem

Stanford MS&E435 Economics of the AI Supercycle | Spring 2026 | Applications, Coding AI

Stanford MS&E435 Economics of the AI Supercycle | Spring 2026 | Building AI Factories

AI in Healthcare Series: Inside the Rise of AI in Healthcare, Open Evidence and Cyber Risks

Stanford CS153 Frontier Systems | Scale, AGI, and the Future of Everything

Bereit

Stanford CS229: Machine Learning Lecture 1 - Andrew Ng (Autumn 2018)