[ 921COENPRMV13 ] VL Probabilistic Models

Es ist eine neuere Version 2022W dieser LV im Curriculum Master's programme Business Informatics 2023W vorhanden.
Workload Education level Study areas Responsible person Hours per week Coordinating university
3 ECTS M1 - Master's programme 1. year Computer Science Gerhard Widmer 2 hpw Johannes Kepler University Linz
Detailed information
Original study plan Master's programme Computer Science 2016W
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 real-world 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, Variable Elimination and Message Passing Algorithms; 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. Semi-directed and undirected Models: Markov Random Fields, Conditional Random Fields, Conditional Bayes Nets. 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
On-site course
Maximum number of participants -
Assignment procedure Direct assignment