FI:PA196 Pattern Recognition - Course Information
PA196 Pattern Recognition
Faculty of InformaticsAutumn 2019
- Extent and Intensity
- 2/2/0. 3 credit(s) (plus extra credits for completion). Type of Completion: zk (examination).
- Teacher(s)
- doc. Ing. Vlad Popovici, PhD (lecturer), doc. RNDr. Petr Matula, Ph.D. (deputy)
- Guaranteed by
- doc. RNDr. Petr Matula, Ph.D.
Department of Visual Computing – Faculty of Informatics
Supplier department: Department of Visual Computing – Faculty of Informatics - Timetable
- Mon 14:00–15:50 A319
- Timetable of Seminar Groups:
- Prerequisites
- At least working knowledge of statistics/probabilities, linear algebra and mathematical analysis are required
- Course Enrolment Limitations
- The course is also offered to the students of the fields other than those the course is directly associated with.
- fields of study / plans the course is directly associated with
- there are 29 fields of study the course is directly associated with, display
- Course objectives
- 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.
- Learning outcomes
- 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.
- Syllabus
- 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
- Literature
- required literature
- HASTIE, Trevor, Robert TIBSHIRANI and 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
- recommended literature
- KUNCHEVA, Ludmila. Combining pattern classifiers: methods and algorithms. John Wiley & Sons, 2004. ISBN 0-471-21078-1. info
- DUDA, Richard O., David G. STORK and Peter E. HART. Pattern classification. 2nd ed. New York, N.Y: John Wiley & Sons, 2001, xx, 654. ISBN 0471056693. info
- not specified
- HEIJDEN, Ferdinand van der, Robert DUIN, Dick de RIDDER and David M J TAX. Classification, Parameter Estimation and State Estimation: An Engineering Approach Using MATLAB. 2004. ISBN 978-0-470-09013-8. info
- Teaching methods
- Lectures; group work on specific topics; laboratory classes.
- Assessment methods
- Written and practical exams; bonuses for attendance and involvement
- Language of instruction
- English
- Further Comments
- Study Materials
The course is taught annually.
- Enrolment Statistics (recent)
- Permalink: https://is.muni.cz/course/fi/autumn2019/PA196