Pattern Recognition
Module EI7358
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.
Whether the module’s courses are offered during a specific semester is listed in the section Courses, Learning and Teaching Methods and Literature below.
| available module versions | |
|---|---|
| SS 2017 | WS 2014/5 |
Basic Information
EI7358 is a semester module in English language at Master’s level which is offered in summer semester.
This module description is valid from SS 2014 to WS 2019/20.
| Total workload | Contact hours | Credits (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
| Type | SWS | Title | Lecturer(s) | Dates | Links |
|---|---|---|---|---|---|
| VO | 2 | Pattern Recognition | Gilg, J. Rigoll, G. |
Wed, 13:15–14:45, N1189 |
|
| UE | 2 | Pattern Recognition Exercise | Gilg, J. Rigoll, G. Teepe, T. |
Fri, 11:30–13:00, N1189 |
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.
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
- 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
- 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.