Checking date: 20/05/2023

Course: 2023/2024

Probabilistic Methods in AI
Master in Applied Artificial Intelligence (Plan: 475 - Estudio: 378)

Coordinating teacher: JIMENEZ RECAREDO, RAUL JOSE

Department assigned to the subject: Statistics Department

Type: Electives
ECTS Credits: 3.0 ECTS


The objective of the course is to offer a complete introduction to the probability theory necessary for the field of artificial intelligence, combining breadth and depth, and offering the basic material as well as the discussion of recent developments in the field.
Skills and learning outcomes
Description of contents: programme
Review of fundamentals of probability theory. Multivariate models: Joint distribution of several variables. Multivariate normal distribution. Linear Gaussian systems. Mix models. Maximum likelihood. Regression and classification with MV. Expectation-maximization algorithm. Model selection criteria. Information theory: Entropy and relative entropy. Linear models: Logistic, linear regression and generalized linear models. Non-parametric models: Classification and clustering with KNN. Probabilistic classifiers. kernel methods. Bagging, random forest, boosting.
Learning activities and methodology
Teaching presentations accompanied by electronic material, such as digital presentations e-learning activities Theoretical-practical lessons, synchronous teaching tutorials Team work Individual student work Home works and
Assessment System
  • % end-of-term-examination 0
  • % of continuous assessment (assigments, laboratory, practicals...) 100
Calendar of Continuous assessment
Basic Bibliography
  • Jason Brownlee . Probability for Machine Learning . Machine Learning Mastery . 2020
  • Kevin Patrick Murphy . Machine Learning: A Probabilistic Perspective . MIT Press. 2012

The course syllabus may change due academic events or other reasons.