Machine Learning II
Probability foundations, Bayesian inference, latent variable models, EM, variational inference, graphical models, sampling, and neural networks.
Study Paths
4Core Map
Key concepts, theorems, probability foundations, and model frameworks.
Practice / Review
Worked examples, inference derivations, EM patterns, and exam-support notes.
Essays / Projects
Analyses, project reports, and research-style write-ups connected to modeling.
Reference / Notation
Formula sheets, notation guides, distributions, and compact reference pages.