This monograph aims at providing an introduction to key concepts, algorithms,
and theoretical results in machine learning. The treatment concentrates on
probabilistic models for supervised and unsupervised learning problems. It
introduces fundamental concepts and algorithms by building on first principles,
while also exposing the reader to more advanced topics with extensive pointers
to the literature, within a unified notation and mathematical framework. The
material is organized according to clearly defined categories, such as
discriminative and generative models, frequentist and Bayesian approaches,
exact and approximate inference, as well as directed and undirected models.
This monograph is meant as an entry point for researchers with a background in
probability and linear algebra.