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Pattern Recognition

Module EI7358

This Module is offered by TUM Department of Electrical and Computer Engineering.

This module handbook serves to describe contents, learning outcome, methods and examination type as well as linking to current dates for courses and module examination in the respective sections.

Module version of WS 2014/5

There are historic module descriptions of this module. A module description is valid until replaced by a newer one.

available module versions
SS 2017WS 2014/5

Basic Information

EI7358 is a semester module in English language at Master’s level which is offered in summer semester.

This Module is included in the following catalogues within the study programs in physics.

  • Catalogue of non-physics elective courses
Total workloadContact hoursCredits (ECTS)
150 h 60 h 5 CP

Content, Learning Outcome and Preconditions

Content

Pattern recognition applications, feature extraction for patterns, data preprocessing, distance classifiers, decision functions, polynomial classifiers, clustering methods, self-organizing maps, Bayes classifiers, Maximum Likelihood methods, probabilistic inference, VC dimension, decision trees and random forests, perceptron, support vector machines.

Learning Outcome

At the end of the module students are able to apply different pattern recognition methods to a range of everyday and scientific problems. They are are able to analyse feature extraction and selection methods. They are able to analyse supervised und unsupervised classification methods, including the training of classifiers with machine learning techniques.

Preconditions

Basic linear algebra; for the exercises, rudimentary programming skills, ideally in Matlab; basic knowledge in statistics and signal representation.

Courses, Learning and Teaching Methods and Literature

Courses and Schedule

TypeSWSTitleLecturer(s)Dates
VO 2 Pattern Recognition Rigoll, G. Wed, 13:15–14:45, N1189
UE 2 Pattern Recognition Exercise Babaee, M. Köpüklü, O. Rigoll, G. Fri, 11:30–13:00, N1189
and singular or moved dates

Learning and Teaching Methods

Learning method:
In addition to the individual methods of the students, lecture contents are repeated and student understanding is facilitated by practical application in exercises. Exercise sheets are provided in advance of the respective tutorial session and should be solved a s (non-mandatory, ungraded) homework; this includes short programming tasks where Matlab templates are provided.

Teaching method:
During the lectures students are instructed in a teacher-centered style.
The exercises are held in a student-centered way.

Media

The following kinds of media are used:
- Presentations with projected slides
- Lecture and tutorial notes
- Downloadable exercises with solutions

Literature

The following literature is recommended:
- R. O. Duda, P. E. Hart, D. G. Stork, Pattern Classification, 2.Auflage, John Wiley & Sons, 2001.
- C. Bishop, Pattern Recognition and Machine Learning, Springer, 2007

Module Exam

Description of exams and course work

In a written exam (75 min) without aids students prove by answering short questions and by performing calculations that they are able to handle feature extraction methods, probabilistic inference, and machine learning techniques.

Exam Repetition

There is a possibility to take the exam in the following semester.

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