CONTENIDO: Machine Learning – what and why?
Introduction
Probability
Generative Models for Discrete Data
Gaussian models
Bayesian statistics
Frequentist statistics
Linear regression
Logistic Regression
Generalized linear models and the exponential family
Directed graphical models (Bayes nets)
Mixture models and the EM algorithm
Latent linear models
Sparse linear models
Kernels
Gaussian processes
Adaptive basis function models
Markov and hidden Markov models
State space models
Undirected graphical models (Markov random fields)
Exact inference for graphical models
Variation inference
More variational inference
Monte Carlo inference
Markov chain Monte Carlo (MCMC) inference
Clustering
Graphical model structure learning
Latent variable models for discrete data
Deep Learning
Notation
Bibliography
