machine-learning-theory
4 free lessons tagged machine-learning-theory across AI. Each one is a short sequence of focused steps with narration and a five-question quiz at the end — take them in any order, no signup required.
What Phase Transitions Mean for Machine Learning
Taking the framework beyond solvable toy models: sharp transitions in real learning, why the loss landscape of a neural network is not the fractured one theory warns about, and what the physics lens genuinely explains.
Message Passing and the Algorithms That Reach the Limit
Belief propagation, the cavity method, and approximate message passing: how physics-derived algorithms achieve the best performance any efficient method can, and how state evolution predicts their behaviour exactly before you run them.
Easy, Hard, and Impossible: The Three Phases
The central result of the field: problems split into three regimes as data increases, and the middle one contains enough information to solve them while no efficient algorithm can. Community detection and planted clique make it concrete.
Planted Problems and the High-Dimensional Limit
Why statistical physics has anything to say about algorithms: planted models with known ground truth, the large-system limit where randomness stops fluctuating, and the Bayes-optimal benchmark that makes hardness measurable.