XP33ROD - Pattern Recognition for doctoral students
Summer semestr 2007/2008, 2 hours lectures, personal assignment equivalent to 2 hours per week.
The lecture is given on Mondays from 9:15 to 10:45 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 which is usually taught in the same semester but is not opened in Summer semester 2007/2008.
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.
Praha, February 18, 2008
Plan of the lectures
Week of a semester
Topic of the lecture
|1||no one||25.02.2008||No lecture. V. Hlaváč is on the business trip abroad.|
|2||V. Hlaváč||3.03.2008||Introduction. Basic concepts. Formulation of the basic tasks solved in pattern recognition. Rehearsal of basic knowledge from probability theory and statistics.|
|3||J. Matas||10.03.2008||Bayesian task. Overview of non-Bayesian tasks.|
|4||V. Hlaváč||17.03.2008||Experimental evaluation of classifiers. Receiver operator curve (ROC).|
|5||no one||24.03.2008||Holiday. Easter Monday.|
|5||V. Mařík||31.03.2008||Structural pattern recognition, a classical approach.|
|6||V. Hlaváč||7.04.2008||Two special useful statistical models. Conditional independence of features. Gaussian models. Strightening of the feature space.|
|7||V. Hlaváč||14.04.2008||Estimation of probabilistic models. Parametric and nonparametric methods.|
|8||V. Hlaváč||21.04.2008||Learning in pattern recognition. VC dimension. Estimate of the needed length of the training sequence.|
|9||V. Hlaváč||28.04.2008||Linear classifiers and their learning.|
|10||V. Hlaváč||5.05.2008||Support vector machines classifiers (SVM).|
|11||V. Hlaváč||12.05.2008||Kernel methods.|
|Recognition of Markovian sequences.|
|13||J. Matas||26.05.2008||Unsupervised learning. Cluster analysis. EM (Expectation Maximization) algorithm.|
|no one||2.06.2008||No lecture because of V. Hlavac's business trip.|
|addition||V. Hlaváč||9.06.2008 15:00||Relation between statistical and structural pattern recognition.|
Students of the subject:
Presentations (It is likely that presentation will be updated before the lecture):
Follow the www pages with my presentation I maintain: in English, in Czech.
Poslední změna 25.05. 2008 V. Hlaváč, email@example.com