Mustererkennung (Pattern Recognition)

Bayesian decision rule
Performance assessment in pattern recognition
Supervised learning with parametric densities
Supervised learning with non-parametric densities, classification
Linear decision functions, single-layer perceptron
Support vector machines (SVMs)
Multi-layer perceptron, neural networks (NNs)
Boosting methods
Non-supervised learning, clustering

Lecturer: Prof. Tim Fingscheidt
Assitant: Björn Möller (Contact person for questions)

Lecture (ET-NT-102):
Contact hours (SWS): 2 h
Time: Monday 13:15 - 14:45 pm (first lecture on monday 19.10.2026)
Location: SN 22.1

Language: german
StudIP Link

Seminar (ET-NT-103):
Contact hours (SWS): 2 h (held as block seminar)
Date: xx.xx. and xx.xx.2025
Time: 9.30 am - 6:00 pm
Location: SN 22.1
Language: german

StudIP Link

Examination type: Presence exam

Date: 03.03.2027

Time: 10:30 a.m. - 12:00 a.m.

Entry admission starts at 10:00 a.m.

Location : ZI 24.1, ZI 24.2

Consultation hour (questions about exam): by agreement with Björn Möller

Information about exam:

Students are requested to register (and, if desired, deregister) at their respective department (facoulty) before the respective deadline. All participants must have a student ID an additionally a different valid ID with a photo.

The exam results are available here.

Review of the examination score: During x:xx - x:xx, XX.XX.XXXX at SN 22.2, Schleinitzstr. 22.

Mustererkennung (Pattern Recognition)

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