Students should recognize the issue of uncertainty inherent in many Artificial Intelligence applications, understand basic methods for dealing with this issue and learn to adopt and comprehend concrete algorithms that implement these methods. The focus in the first half of the semester will be on reasoning under uncertainty, whereas the second half will deal with learning.

The course consists on a mix between theoretical lectures and practical exercises. Slides and teaching will be in English.eLearningMoodle

Provides an introduction to selected methods for dealing with uncertainty in Artificial Intelligence and Knowledge-Based Systems.TopicsUncertainty in AI SystemsBayesian Inference and Bayesian NetworksMachine Learning

Adnan Darwiche. Modeling and Reasoning with Bayesian Networks. Cambridge University Press. 2009 P. Tan, M. Steinbach, V. Kumar. Introduction to Data Mining. Pearson. 2006 Stuart Russell and Peter Norvig: Artificial Intelligence: A modern approach. Prentice Hall, 2009 Judea Pearl: Probabilistic Reasoning in Intelligent Systems - Networks of Plausible Inference. Morgan Kaufmann Publishers, Inc. 1988 D. Koller, N. Friedman. Probabilistic Graphical Models: Principles and Techniques. The MIT Press. 2009 D. Barber. Bayesian Reasoning and Machine Learning. Cambridge University Press. 2012 T. Mitchell. Machine Learning. McGraw Hill. 1997

Written Exam at the end of the lectures + Minitests during the semester

All the topics treated during the lectures. 

90% of the score will be given by the performance of the final exam. 10% comes from the performance achieved during the lectures, evaluated through mini-tests and participation.