Data Mining and Probabilistic Reasoning (Sommersemester 2012)

Lecturer:

General Information

• Weekly Hours: 2
• Credits: 3
• Graded: yes
• Enrolment Deadline: 1.4.2012 - 25.4.2012
• Teaching Form: VU
• Enrolment Type: Compulsory Elective Module

Programs, Module Groups & Modules

Description

Probability theory & statistical methods
Information theory
Classification: introduction
Hierachical classifiers
Linear classifiers
Clustering: introduction
Hierarchical clustering
Co-clustering, topic models
Graphical models: introduction
Directed vs. undirected models
Factor graphs & inference
Example: Hidden Markov Models

Literature

I. H. Witten, E. Frank, M. A. Hall: Data Mining Practical Machine Learning Tools and Techniques (Chapters 1 - 6)

C. Bishop: Pattern Recognition and Machine Learning (Chapters 1, 2, 4, 8, 9)

D. J. C. MacKay: Information Theory, Inference and Learning Algorithms (Chapters 1 - 6)

Examination

Form of exam: oral exam at the end of the term

Condition for exam admission: oral presentation of at least two solutions during the tutorials

Dates

Lectures: Mondays 15:15 - 16:45, room A-2.2

Exercises: biweekly, Thursdays 17:00 - 18:40, room A-2.2

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