The course provides an introduction to the basics of machine learning from a probabilistic perspective. The aim is to allow the student to develop the ability to design inference and learning models and methods in a Bayesian framework. The course begins with a review of probability, mathematics, and optimisation, followed by a discussion of the most common probabilistic models for discrete and continuous data and then models and methods for sequences. The main techniques of exact and approximate inference using a representation based on graphical models are presented below, including, among others, MCMC and variational inference methods. The course ends with the application of the above to deep generative models.

1. Introduction to probabilistic machine learning. 2. Models for discrete and continuous data. 3. Markovian and state-space models. 4. Graphical models. Exact and approximate inference in graphical models. 5. Deep generative models.

AF3 Theoretical practical classes AF4 Laboratory practices AF5 Tutorials AF6 Team work AF7 Student individual work AF8 Partial and final exams Activity code total hours number presential hours number % Student Presence AF3 134 134 100% AF4 42 42 100% AF5 24 0 0% AF6 120 0 0% AF7 248 0 0% AF8 16 16 100% subject total: 600 184 30.66%