Module Handbook

  • Dynamischer Default-Fachbereich geändert auf INF

Module INF-76-61-M-6

Probabilistic graphical models (M, 4.0 LP)

Module Identification

Module Number Module Name CP (Effort)
INF-76-61-M-6 Probabilistic graphical models 4.0 CP (120 h)

Basedata

CP, Effort 4.0 CP = 120 h
Position of the semester 1 Sem. in SuSe
Level [6] Master (General)
Language [EN] English
Module Manager
Lecturers
Area of study [INF-KI] Intelligent Systems
Reference course of study [INF-88.79-SG] M.Sc. Computer Science
Livecycle-State [NORM] Active

Courses

Type/SWS Course Number Title Choice in
Module-Part
Presence-Time /
Self-Study
SL SL is
required for exa.
PL CP Sem.
2V+1U INF-76-61-K-6
Probabilistic graphical models
P 42 h 78 h
U-Schein
ja PL1 4.0 SuSe
  • About [INF-76-61-K-6]: Title: "Probabilistic graphical models"; Presence-Time: 42 h; Self-Study: 78 h
  • About [INF-76-61-K-6]: The study achievement "[U-Schein] proof of successful participation in the exercise classes (ungraded)" must be obtained.
    • It is a prerequisite for the examination for PL1.

Examination achievement PL1

  • Form of examination: written exam (Klausur) (60-90 Min.)
  • Examination Frequency: each summer semester
  • Examination number: 67661 ("Probabilistic graphical models")

Evaluation of grades

The grade of the module examination is also the module grade.


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

Competencies / intended learning achievements

Students will
  • learn to translate ML problems into the language of graphical models
  • learn to approach problems by sketching relations between variables using a graph and defining probability distributions
  • obtain a basic understanding of available learning and inference algorithms for graphical models
  • get insight into current research directions in graphical models

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 of the module (informal)

None

Requirements for attendance of the module (formal)

None

References to Module / Module Number [INF-76-61-M-6]

Course of Study Section Choice/Obligation
[INF-88.79-SG] M.Sc. Computer Science [Specialisation] Specialization 1 [WP] Compulsory Elective