Probabilistic Models (VL)

This class is usually taught by Gerhard Widmer in the winter term. The class is taught in English.

Information for the current semester (if available):

{{ labelInLang('cid') }} {{ labelInLang('title') }} {{ labelInLang('registration') }} {{ labelInLang('type') }} {{ labelInLang('hours') }} {{ labelInLang('teachers') }} {{ labelInLang('rhythm') }}
{{ item._id }} ({{ item.term }}) {{ item.title }}
{{ labelInLang('moreinfo') }}
{{ labelInLang('expand') }} {{ labelInLang('collapse') }}
{{ labelInLang('register') }} {{ item.type }} {{ item['hours-per-week'] }} {{ teacher.firstname }} {{ teacher.lastname }} {{ item.teachers.teacher.firstname }} {{ item.teachers.teacher.lastname }} {{ item.rhythm }}
{{ item._id }} ({{ item.term }})
{{ labelInLang('title') }} {{ item.title }}
{{ labelInLang('moreinfo') }}
{{ labelInLang('expand') }} {{ labelInLang('collapse') }}
{{ labelInLang('registration') }} {{ labelInLang('register') }}
{{ labelInLang('type') }} {{ item.type }}
{{ labelInLang('hours') }} {{ item['hours-per-week'] }}
{{ labelInLang('teachers') }} {{ teacher.firstname }} {{ teacher.lastname }} {{ item.teachers.teacher.firstname }} {{ item.teachers.teacher.lastname }}
{{ labelInLang('rhythm') }} {{ item.rhythm }}
 

Motivation:

Here is a little set of Introduction & Motivation Slides ... 

Goals and Contents of this Class:

This course is a gentle introduction to one of the most important and central classes of methods in present-day Artificial Intelligence. It will introduce students to the basic concepts of Probabilistic Graphical Models as representations of uncertain knowledge in complex domains. All three aspects related to such models will be covered: model semantics, inference, and learning. In particular, the following topics will be covered (in more or less detail):

  • Elementary Concepts: Basics on Probability Distributions, Density Functions, Probabilistic Reasoning and Inference.
  • Bayesian Networks: Representation, Semantics, Conditional Independence, Factorisation.
  • Inference in Bayesian Networks: Exact Inference, Variable Elimination Algorithms; Approximate Inference via Stochastic Sampling, Markov Chain Monte Carlo (MCMC) Methods.
  • Learning Bayesian Networks: Parameter Learning, Structure Learning, Learning Generative vs. Discriminative Models.
  • Modelling and Reasoning about Temporal Phenomena: Kalman Filters, Hidden Markov Models, Dynamic Bayes Nets, Particle Filters.
  • (Briefly, if there is time:) Semi-directed and Undirected Models: Conditional Bayes Nets, Conditional Random Fields, Markov Random Fields.
  • Selected Applications of Probabilistic Graphical Models.

It is strongly recommended to take this VO together with the "Practical Excercises in Probabilistic Models" (UE, 1h) in the same semester. There, the students will perform practical experiments with some of the methods taught in the VO. 
 

Teaching materials:

Pdf versions of the Powerpoint slides used in the lecture will be made available via KUSSS (weekly).

Recommended reading (will not be needed if the lectures are attended on a regular basis):

Koller, Daphne and Friedman, Nir (2009). 
Probabilistic Graphical Models: Principles and Techniques. Cambridge, MA: MIT Press. 

Russell, Stuart J. and Norvig, Peter (2003). 
Artificial Intelligence: A Modern Approach. Englewood Cliffs, NJ: Prentice Hall. 
 

Questions, suggestions, complaints, etc. to:

Gerhard Widmer
Tel. 2468-4701 
gerhard dot widmer at jku dot at