Linear algebra. Multivariable calculus. Statistics. Introduction to Machine Learning (or similar)

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.

LEARNING ACTIVITIES AF3 Theoretical, practical classes AF4 Laboratory practices AF5 Tutorials AF6 Teamwork AF7 Student individual work AF8 Partial and final exams METHODOLOGY MD1: Lectures in a class by the teacher supported by computer and audiovisual media, in which the main concepts of the subject are developed, and a bibliography is provided to complement the student's learning. MD2: Critical reading of texts recommended by the subject teacher MD3: Resolution of practical cases, problems, etc., posed by the teacher individually or in groups MD4: Presentation and discussion in class, under the teacher's moderation, of topics related to the content of the subject, as well as practical cases MD5: Preparation of work and reports individually or in groups TUTORING REGIME There will be 2 hours a week of tutorials for students, and the teacher will be available in his office.