## PA196 Pattern Recognition

Fakulta informatiky
podzim 2020
Rozsah
2/2/0. 3 kr. (plus ukončení). Ukončení: zk.
Vyučováno online.
Vyučující
doc. Ing. Vlad Popovici, PhD (přednášející), doc. RNDr. Petr Matula, Ph.D. (zástupce)
Garance
doc. RNDr. Petr Matula, Ph.D.
Katedra vizuální informatiky - Fakulta informatiky
Dodavatelské pracoviště: Katedra vizuální informatiky - Fakulta informatiky
Rozvrh
St 16:00–17:50 A318
• Rozvrh seminárních/paralelních skupin:
PA196/01: St 18:00–19:50 B130, V. Popovici
At least working knowledge of statistics/probabilities, linear algebra and mathematical analysis are required
Omezení zápisu do předmětu
Předmět je nabízen i studentům mimo mateřské obory.
Mateřské obory/plány
předmět má 29 mateřských oborů, zobrazit
Cíle předmětu
By successful completion of the course, the students will: (i) have solid understanding of the principles of pattern recognition; (ii) master the main methods for model performance estimation; (iii) have a good grasp of different parametric and non-parametric methods for classification; (iv) understand the main classification methods; (v) have a working knowledge of kernel methods; (vi) understand and master the performance estimation techniques (vii) have hands-on experience of using pattern recognition methods in computer vision and biomedical applications.
Výstupy z učení
By successful completion of the course, the students will: (i) have solid understanding of the principles of pattern recognition; (ii) master the main methods for model performance estimation; (iii) have a good grasp of different parametric and non-parametric methods for classification; (iv) understand the main classification methods; (v) have a working knowledge of kernel methods; (vi) understand and master the performance estimation techniques (vii) have hands-on experience of using pattern recognition methods in computer vision and biomedical applications.
Osnova
• 1. Introduction: problem setting; distances, metrics, similarity; Bayesian decision theory 2. Non-parametric methods: density estimation; nearest neighbor methods 3. Classification performance: performance criteria; performance estimation; confidence intervals; classifier comparison 4. Linear discriminants: decision surfaces; parameter optimization; shrinkage, penalized regression, optimal separating hyperplanes; SVM 5. Ensemble methods: fusion of labels or continuous outputs; parallel and cascaded systems; bagging and boosting; AdaBoost 6. One-class classifiers: Gaussian mixtures; support vector density estimators; outlier detection 7. Dimensionality reduction: feature selection; PCA, ICA, non linear PCA 8. Unsupervised learning/clustering: principles; mixture of densities; hierarchical clustering
Literatura
povinná literatura
• HASTIE, Trevor, Robert TIBSHIRANI a J. H. FRIEDMAN. The elements of statistical learning : data mining, inference, and prediction. 2nd ed. New York, N.Y.: Springer, 2009. xxii, 745. ISBN 9780387848570. info
doporučená literatura
• KUNCHEVA, Ludmila. Combining pattern classifiers: methods and algorithms. : John Wiley & Sons, 2004. ISBN 0-471-21078-1. info
• DUDA, Richard O., David G. STORK a Peter E. HART. Pattern classification. 2nd ed. New York, N.Y: John Wiley & Sons, 2001. xx, 654. ISBN 0471056693. info
neurčeno
• HEIJDEN, Ferdinand van der, Robert DUIN, Dick de RIDDER a David M J TAX. Classification, Parameter Estimation and State Estimation: An Engineering Approach Using MATLAB. 2004. ISBN 978-0-470-09013-8. info
Výukové metody
Lectures; group work on specific topics; laboratory classes.
Metody hodnocení
Written and practical exams; bonuses for attendance and involvement
Vyučovací jazyk
Angličtina
Další komentáře
Studijní materiály
Předmět je vyučován každoročně.
Předmět je zařazen také v obdobích podzim 2014, podzim 2015, podzim 2016, podzim 2017, podzim 2018, podzim 2019.
• Statistika zápisu (nejnovější)