Module Handbook

  • Dynamischer Default-Fachbereich geändert auf INF

Course INF-76-61-K-6

Probabilistic graphical models (2V+1U, 4.0 LP)

Course Type

SWS Type Course Form CP (Effort) Presence-Time / Self-Study
- K Lecture with exercise classes (V/U) 4.0 CP 78 h
2 V Lecture 28 h
1 U Exercise class (in small groups) 14 h
(2V+1U) 4.0 CP 42 h 78 h

Basedata

SWS 2V+1U
CP, Effort 4.0 CP = 120 h
Position of the semester 1 Sem. in SuSe
Level [6] Master (General)
Language [EN] English
Lecturers
Area of study [INF-KI] Intelligent Systems
Livecycle-State [NORM] Active

Possible Study achievement

  • Verification of study performance: proof of successful participation in the exercise classes (ungraded)
  • Details of the examination (type, duration, criteria) will be announced at the beginning of the course.

Contents

  • Bayesian Networks
  • Markov Random Fields
  • Exact Inference: Variable Elimination
  • Exact Inference: Clique Trees
  • Belief Propagation
  • Message Passing
  • Parameter Learning for Fully/Partially Observed Models
  • Approximate Inference for Graphical Models
  • Structure Learning
  • Causality

Literature

  • Daphne Koller, Nir Friedman: Probabilistic Graphical Models Principles and Techniques, 2009.
  • Christopher M. Bishop: Pattern Recognition and Machine Learning, 2006.
  • Kevin P. Murphy: Machine Learning: A Probabilistic Perspective, 2012.

Requirements for attendance (informal)

Requirements for attendance (informal)
  • Basic understanding of statistics and probability theory
  • Nice to have: Machine learning I and II

Requirements for attendance (formal)

None

References to Course [INF-76-61-K-6]

Module Name Context
[INF-76-61-M-6] Probabilistic graphical models P: Obligatory 2V+1U, 4.0 LP
Course-Pool Name
[INF-KI_V-KPOOL-6] Lectures of the teaching area Intelligent Systems