
Detailed information 
Original study plan 
Master's programme Computer Science 2019W 
Objectives 
In this course, students will learn the basic concepts of a large and centrally important class of methods in modern Artificial Intelligence: Probabilistic Graphical Models, used for representing and reasoning about uncertain information and knowledge in complex realworld scenarios. All three aspects related to such models will be covered: model semantics, inference, and learning. Both basic concepts and specific algorithms will be taught, and all methods will be derived in a mathematically rigorous way.

Subject 
Elementary Concepts: Probability Distributions, Density Functions, (conditional) independence, Probabilistic Reasoning and Inference. Bayesian Networks: Representation, Semantics, Factorisation.
Inference in Bayesian Networks: Exact Inference; Approximate Inference: Stochastic Sampling, Markov Chain Monte Carlo (MCMC) Methods.
Learning Bayesian Networks: Parameter Learning, Structure Learning, Learning
Generative vs. Discriminative Models (optional).
Special Types of Bayes Nets: Linear Gaussian Network Models.
Modelling and Predicting Temporal Processes: Dynamic Bayes Networks, Hidden Markov Models, Kalman Filters; Particle Filters.
Selected Applications of Probabilistic Graphical Models.

Criteria for evaluation 
Written exam at the end of the semester.

Methods 
Slide presentation with case studies on the blackboard

Language 
English 
Study material 
Koller, Daphne, and Friedman, Nir (2009). Probabilistic Graphical Models. Principles and Techniques. Cambridge, MA: MIT Press.
Russell, Stuart J. and Norvig, P. (2010). Artificial Intelligence: A Modern Approach (3rd Edition). Upper Saddle River, NJ: Prentice Hall.

Changing subject? 
No 
