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ai deep-dive Reading Stanford CS229

EM Algorithms: From Gaussian Mixtures to VAEs

Chapter 11 starts from soft assignments in Gaussian mixtures, uses Jensen's inequality to construct the ELBO, interprets EM as alternating maximization over a variational distribution and model parameters, and extends the idea to VAEs through approximate posteriors and reparameterization.

#cs229#machine-learning#unsupervised-learning#em-algorithm#variational-inference#vae
learning guide Reading Berkeley CS285 Spring 2026

Berkeley CS285 L11–18: From Variational Inference and LLM RL to Offline RL

L11–18 connect control as inference, LLM RL, model-based RL, and offline RL, with HW4 and HW5 providing two compute-intensive implementations.

#cs285#berkeley#variational-inference#llm-rl#offline-rl
ai deep-dive Reading Stanford CS228

Stanford CS228: The Prerequisites Are One Sentence About Probability and Algorithms — But the Course Hasn't Run in Two Years

CS228's official prerequisite is a single line — 'basic probability theory and algorithm design and analysis' — with no named course. But ExploreCourses shows it was last offered in Winter 2024, and the next slot, Winter 2027, still has a blank instructor field. What a self-learner can actually get is cs228-notes: 16 chapters, complete, last touched in June 2025.

#cs228#ai-course#stanford#probabilistic-graphical-models#bayesian-network#variational-inference