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Curriculum

Modul CS4220 T

Module part: Pattern Recognition (MEa)

Duration:


1 Semester
Turnus of offer:


not available anymore
Credit points:


4
Course of studies, specific field and terms:
  • Master Computer Science 2019 (module part), Module part, Arbitrary semester
  • Master MES 2020 (module part), computer science / electrical engineering, Arbitrary semester
  • Master Entrepreneurship in Digital Technologies 2020 (module part), Module part, Arbitrary semester
  • Master Biophysics 2019 (module part), advanced curriculum, 2nd semester
  • Master IT-Security 2019 (module part), Module part, 1st or 2nd semester
  • Master Computer Science 2014 (module part), advanced curriculum, Arbitrary semester
  • Master Entrepreneurship in Digital Technologies 2014 (module part), Module part, Arbitrary semester
  • Master MES 2014 (module part), computer science / electrical engineering, 1st semester
  • Master Computer Science 2014 (Module part of a compulsory module), specialization field robotics and automation, Arbitrary semester
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:
Teachers:
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)

Letzte Änderung:
24.11.2022