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Stanford AI Programs Info

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Stanford CS229 Machine Learning | Spring 2026 | Lecture 1: Introduction Jul 29, 2026 · 36:59 The course emphasizes understanding the mathematical foundations of ML, covering Supervised, Unsupervised, and Reinforcement Learning. A key focus is optimizing algorithms for hardware compatibility.

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Stanford CS153 Frontier Systems | Teaching AI to Touch Atoms Sep 29, 2026 · 42:09 Periodic Labs is applying AI to physical science, creating a closed-loop system that links computational prediction, robotic synthesis, and experimental verification to accelerate materials discovery. Stanford CS229 Machine Learning | Spring 2026 | Lecture 20: GMM (EM), PCA Jul 31, 2026 · 1:18:56 The lecture details advanced RL techniques for LLMs, covering Policy Gradient derivations and the stabilization mechanism of Proximal Policy Optimization (PPO). It shows how to model Chain-of-Thought prompting as an MDP problem. Stanford CS229 Machine Learning | Spring 2026 | Lecture 16: Basic Concept in RL, Policy Gradient Jul 31, 2026 · 1:13:19 The lecture reviews advanced Transformer optimizations, covering Grouped Query Attention (GQA) to reduce KV cache memory and Mixture of Experts (MoE) for scaling parameters while controlling compute. It also details In-Context Learning and SFT.

Stanford Online

Stanford CS153 Frontier Systems | Teaching AI to Touch Atoms

Periodic Labs is applying AI to physical science, creating a closed-loop system that links computational prediction, robotic synthesis, and experimental verification to accelerate materials discovery.

Read article

Stanford Online

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

The lecture details advanced RL techniques for LLMs, covering Policy Gradient derivations and the stabilization mechanism of Proximal Policy Optimization (PPO). It shows how to model Chain-of-Thought prompting as an MDP problem.

Read article

Stanford Online

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

The lecture reviews advanced Transformer optimizations, covering Grouped Query Attention (GQA) to reduce KV cache memory and Mixture of Experts (MoE) for scaling parameters while controlling compute. It also details In-Context Learning and SFT.

Read article

Stanford Online

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

The course emphasizes understanding the mathematical foundations of ML, covering Supervised, Unsupervised, and Reinforcement Learning. A key focus is optimizing algorithms for hardware compatibility.

Read article