XP33ROD - Pattern Recognition for doctoral students
Summer semestr 2008/2009, 2 hours lectures, personal assignment equivalent to 2 hours exercises per week.
The lecture is given on Mondays from 14:30 to 16:00 in the seminar room G205, building G of the Faculty of Electrical Engineering at the Czech Technical University Campus at Karlovo náměstí. If there is one student enrolled who does not understand Czech then the lectures will be given in English, otherwise in Czech. Most of the presentations are in English too.
The subject XP33ROD je intended as an introductory one for doctoral students who did not pass the subject 33RPZ Pattern Recognition which is taught by Doc. Ing. Jiří Matas, PhD. in the MSc study program or those who like to see the topic through eyes of another teacher. For others, a more advanced subject XP33ROZ is available. Interested students have to contact Jiří Matas.
prof. Ing. Václav Hlaváč,
CSc., prof. Ing. Vladimír Mařík, DrSc.
Katedra kybernetiky, Fakulta elektrotechnická ČVUT
Exercises instructor: prof. Ing. Václav Hlaváč, CSc.
Students have two options how to pass exercises. One of the two options or both can be selected.
Praha, February 16, 2009
Plan of the lectures
Week of a semester
Topic of the lecture
|1||V. Hlaváč||2.03.2009||Introduction. Basic concepts. Formulation of the basic tasks solved in pattern recognition. Rehearsal of basic knowledge from probability theory and statistics.|
|2||V. Hlaváč||9.03.2009||Bayesian task.Non-Bayesian tasks.|
|3||V. Hlaváč||16.03.2009||Two special useful statistical models. Conditional independence of features. Gaussian models. Strightening of the feature space.|
|4||V. Hlaváč||23.03.2009||Experimental evaluation of classifiers. Receiver operator curve (ROC).|
|5||V. Franc||30.03.2009||Estimation of probabilistic models. Parametric and nonparametric methods.|
|6||V. Hlaváč||6.04.2009||Learning in pattern recognition. VC dimension. Estimate of the needed length of the training sequence.|
|7||No one||13.04.2009||Holiday. Easter Monday.|
|8||V. Hlaváč||20.04.2009||Linear classifiers and their learning. Support vector machines classifiers (SVM).|
|9||V. Hlaváč||27.04.2009||Kernel methods.|
|10||V. Hlaváč||4.05.2009||Unsupervised learning. Cluster analysis. EM (Expectation Maximization) algorithm.|
|11||V. Hlaváč||11.05.2009||Intro to structural methods embedded into the statistical framework. Recognition of Markovian sequences.|
|12||V. Hlaváč||18.05.2009||Recognition of Markovian sequences (continuation). 2D grammars for images.|
|Structural pattern recognition, a classical approach.|
|14||V. Mařík||1.06.2009||Experiences learned from practial implementations of pattern recognition methods.|
Presentations (It is likely that presentation will be updated before the lecture): Follow the www pages with presentations V. Hlavac maintains: in English, in Czech. Presentation of V. Franc about probability distributions estimation, click here.
Poslední změna 02.04. 2009 V. Hlaváč, email@example.com