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Module guide

Modul CS4220-KP04, CS4220

Pattern Recognition (Muster)

Duration:


1 Semester
Turnus of offer:


not available anymore
Credit points:


4
Course of studies, specific field and terms:
  • Master MES 2020 (optional subject), medical engineering science, Arbitrary semester
  • Master Media Informatics 2020 (optional subject), computer science, Arbitrary semester
  • Master MES 2014 (optional subject), medical engineering science, Arbitrary semester
  • Master Robotics and Autonomous Systems 2019 (optional subject), Elective, 1st or 2nd semester
  • Master CLS 2016 (compulsory), mathematics, 2nd semester
  • Master Medical Informatics 2019 (optional subject), Medical Data Science / Artificial Intelligence, 1st or 2nd semester
  • Master Medical Informatics 2014 (optional subject), medical image processing, 1st or 2nd semester
Classes and lectures:
  • Pattern Recognition (exercise, 1 SWS)
  • Pattern Recognition (lecture, 2 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:
  • Written or oral exam as announced by the examiner
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:

Prerequisites for attending the module:
- None

Prerequisites for the exam:
- Successful completion of homework assignments during the semester (at least 50% of max. points) and successful project task.

Modul exam:
- CS4220-L1:Pattern Recognition, written exam, 90 Min, 100% of modul grade

Letzte Änderung:
25.8.2023