Module CS4220 T
Module part: Pattern Recognition (MEa)
Duration
1 Semester
Turnus of offer
not available anymore
Credit points
4
Course of studies, specific fields and terms:
- Master Computer Science 2019, module part, Module part
- Master MES 2020, module part, computer science / electrical engineering
- Master Entrepreneurship in Digital Technologies 2020, module part, Module part
- Master IT-Security 2019, module part, Module part
- Master Computer Science 2014, module part, advanced curriculum
- Master Entrepreneurship in Digital Technologies 2014, module part, Module part
- Master MES 2014, module part, computer science / electrical engineering
- Master Computer Science 2014, Module part of a compulsory module, specialization field robotics and automation
Classes and lectures:
- Pattern Recognition (lecture, 2 SWS)
- Pattern Recognition (exercise, 1 SWS)
Workload:
- 20 hours exam preparation
- 45 hours in-classroom work
- 55 hours private studies
Contents of teaching:
- Introduction to probability theory
- Principles of feature extraction and pattern recognition
- Bayes decision theory
- Discriminance functions
- Neyman-Pearson test
- Receiver Operating Characteristic
- Parametric and nonparametric density estimation
- kNN classifiers
- Linear classifiers
- Support vector machines and kernel trick
- Random Forest
- Neural Nets
- Feature reduction and feature transforms
- Validation of classifiers
- Selected application scenarios: acoustic scene classification for the selection of hearing-aid algorithms, acoustic event recognition, attention classification based on EEG data, speaker and emotion recognition
Qualification-goals/Competencies:
- Students are able to describe the main elements of feature extraction and pattern recognition.
- They are able to explain the basic elements of statistical modeling.
- They are able to use feature extraction, feature reduction and pattern classification techniques in practice.
Grading through:
- exam type depends on main module
Responsible for this module:
Literature:
- R. O. Duda, P. E. Hart, D. G. Storck : Pattern Classification New York: Wiley
Language:
- offered only in German
Notes:
Admission requirements for the module:- None
Admission requirements for the examination:
- Successful completion of the exercises during the semester (at least 50% of the achievable points).
Module Exam:
- CS4220-L1: Pattern Recognition, written exam, 90 min, 100% of module grade.
(Is equal to CS4220SJ14)
(Is module part of CS4510, CS4290, CS5274-KP08)
Last Updated:
28.08.2023