Inhalt

[ 921COENPRMV13 ] VL Probabilistic Models

Versionsauswahl
Es ist eine neuere Version 2021W dieser LV im Curriculum Master's programme Business Informatics 2021W 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 2021S
Objectives After this class, students will know and understand 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. Students will understand how such models can be used for problem modeling and solving, and what their limitations are.
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 (maximum likelihood and Bayesian estimation), Structure Learning, Generative vs. Discriminative Models. 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 Lecture series with written materials (presentation slides) provided regularly in electronic form.
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