Probabilistic graphical models - NAIL104
Title: Pravděpodobnostní grafické modely
Guaranteed by: Department of Theoretical Computer Science and Mathematical Logic (32-KTIML)
Faculty: Faculty of Mathematics and Physics
Actual: from 2012
Semester: winter
E-Credits: 3
Hours per week, examination: winter s.:2/0, Ex [HT]
Capacity: unlimited
Min. number of students: unlimited
4EU+: no
Virtual mobility / capacity: no
State of the course: taught
Language: Czech, English
Teaching methods: full-time
Guarantor: Mgr. Marta Vomlelová, Ph.D.
Teacher(s): Mgr. Marta Vomlelová, Ph.D.
Class: Informatika Mgr. - Teoretická informatika
Classification: Informatics > Theoretical Computer Science
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Annotation -
The course extends the basics of probabilistic graphical models introduced in the NAIL070 Artificial Intelligence 2 course: Bayesian networks and their extensions (DBN, OOBN), decision graphs, partially observable markov decision processes (POMDP) and conditional random fields. We focus on the modelling languages and their evaluation methods. We touch also some applications.
Last update: T_KTI (03.05.2012)
Aim of the course -

The course gives an introduction to probabilistic graphical models. The students will learn the following formal models, evaluation and model learning algorithm, application areas.

Last update: Vomlelová Marta, Mgr., Ph.D. (14.05.2021)
Course completion requirements -

The exam consists of a written preparation and an oral part. The requirements are given by the course syllabus.

Last update: Vomlelová Marta, Mgr., Ph.D. (07.06.2019)
Literature -
  • Sucar, Luis Enrique: Probabilistic graphical models: principles and applications, Springer, 2021
  • S. Hojsgaard, D. Edwards, S. Lauritzen: Graphical Models with R, Springer 2012
  • Finn V. Jensen, Thomas D. Nielsen: Bayesian Networks and Decision Graphs, Springer 2007
  • Leslie Pack Kaelbling, Michael L. Littman, and Anthony R. Cassandra. Planning and acting in partially observable stochastic domains. Artificial Intelligence, Volume 101, pp. 99-134, 1998
  • John Lafferty, Andrew McCallum, Rernando Pereira: Conditional random fields: Probabilistic models for segmenting and labeling sequence data, Morgan Kaufmann 2001, pp. 282-289

Last update: Vomlelová Marta, Mgr., Ph.D. (25.09.2024)
Requirements to the exam -

The exam consists of a written preparation and an oral part. The requirements are given by the course syllabus.

Last update: Vomlelová Marta, Mgr., Ph.D. (07.06.2019)
Syllabus -

1) Introduction, Conditional independence, Bayesian Classifiers,

2) Bayesian networks, Hidden Markov Models

3) advanced evaluation methods: d-separation, junction tree, message passing scheme,

4) Bayesian network learning,

5) undirected graphical models,

6) decision graphs,

7) POMDP - Partially Observable Markov Decision Problems,

8) variational approximate inference

9) applications.

Basic introduction into the Python libraries pgmpy, bayespy.

Last update: Vomlelová Marta, Mgr., Ph.D. (21.05.2025)