Pattern Recognition
| Dozent: | Prof. Dr.-Ing. Gerhard Rigoll |
| Assistent: | Johannes Gilg, M.Sc. |
| Turnus: | SS |
| Zielgruppe: | Wahlmodul zur fachlichen Vertiefung, MSEI, MSNE |
| ECTS: | 5 ECTS |
| Umfang: | 2 VO / 2 UE |
| Zeit & Ort: | Lecture: Wednesday, 13:15 - 14:45, N1189 starting at 19.04.2023 |
Exam in SS2023
The exam in Pattern Recognition takes place on July 2023
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.
Course Outline
From unlocking your phone with your fingerprint to speech-controlled 'personal assistants', to automated diagnosis of life-threatening diseases and to highly autonomous driving - Pattern Recognition is everywhere. While many of the recent breakthroughs in Pattern Recognition applications have been enabled by leaps in computational power and very large data sets, the fundamental algorithms and concepts are actually quite simple and mostly have been around for decades. In this lecture with its accompanying exercise, we will cover the following topics:
- 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
Complementary Books
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