Covers the Curie-Weiss model in Statistical Physics, including magnetization probability, free entropy, and the cavity method.
Explores properties of clusters and colorability threshold in graph coloring, including average connectivity and rigidity.
Explores deep learning fundamentals, including image classification, neural network working principles, and machine learning challenges.
Explores the variational method in the Random Field Ising Model, discussing the cost function, algorithmic questions, Gibbs inequality, and the Gibbs free energy.
Explores Belief Propagation in graphical models, factor graphs, spin glass examples, Boltzmann distributions, and graph coloring properties.
Explores Belief Propagation on trees, discussing cavity marginals, message passing algorithms, and the computation of free entropy.
Explores a variety of open problems in graph theory and computational complexity, challenging students to analyze and solve complex issues.
Explores the Spike Wigner model, Bayesian denoising, state evolution, and spectral methods in matrix analysis.
