Explores properties of clusters and colorability threshold in graph coloring, including average connectivity and rigidity.
Explores Belief Propagation in graphical models, factor graphs, spin glass examples, Boltzmann distributions, and graph coloring properties.
Covers the basics of information theory, entropy, and fixed points in graph colorings and the Ising model.
Covers CNNs, RNNs, SVMs, and supervised learning methods, emphasizing the importance of tuning regularization and making informed decisions in machine learning.
Covers the computation of Bethe free entropy and the interpretation of messages between variables and factors.
