Explores maximum likelihood inference, comparing models based on likelihood ratios and demonstrating with a coin example.
Explores knowledge inference for graphs, discussing label propagation, optimization objectives, and probabilistic behavior.
Covers the properties of the exponential family and the estimation of parameters.
Covers the basics of information theory, entropy, and fixed points in graph colorings and the Ising model.
Explores Belief Propagation in graphical models, factor graphs, spin glass examples, Boltzmann distributions, and graph coloring properties.
