P33ROD - Pattern Recognition for doctoral students

Summer semestr 2006/2007, 2 hours lectures, personal assignment equivalent to 2 hours per week.

The lecture is given on Thursdays 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. The lectures are given in English because there are students enrolled who do not understand Czech.

The subject P33ROD 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 magister study program. For doctoral students knowing basic pattern recognition, a more advanced subject  P33ROZ is available which is taught in the same semester.

Lecturers:  

prof. Ing. Václav Hlaváč, CSc.,  prof. Ing. Vladimír Mařík, DrSc.
Katedra kybernetiky, Fakulta elektrotechnická ČVUT
hlavac@cmp.felk.cvut.cz, marik@fel.cvut.cz 


 Exercises instructor:  prof. Ing. Václav Hlaváč, CSc.

Students have two options how to pass exercises.

  1. Participate in the exercises taught for master students in the subject 33RPZ. Make your arrangements with the instructors of the subject. The content of exercises can be modified according to previous experience of the student and according to student's research interest.
  2. Solve an individual assignment which is related to the subject. This option has to be negotiated in advanced with V. Hlaváč.

Praha, February 26, 2007

Plan of the lectures

Week of a semester

Lecturer

Date

Topic of the lecture
1 V. Hlaváč 1.03.2007 Introduction. Basic concepts. Formulation of the basic tasks solved in pattern recognition. Rehearsal of basic knowledge from probability theory and statistics.
2 V. Hlaváč 8.03.2007 Bayesian task. Overview of non-Bayesian task.
3 V. Mařík 15.03.2007 Structural pattern recognition. (Lecture will be given in Czech, non-Czech speaking students will be given replacement training by prof. V. Mařík)
4 V. Hlaváč 22.03.2007 Two special statistical models. Conditional independence of features. Gaussian models. Strightening of the feature space.
    29.03.2007 No lecture because V. Hlaváč is on business trip to France..
5 V. Hlaváč 5.04.2007 Estimation of probabilistic models. Parametric and nonparametric methods.
6 V. Hlaváč 5.04.2007 Learning in pattern recognition. VC dimension. Estimate of the needed length of the training sequence. (extra lecure from 12:45 to 14:15 to compensate for the canceled lecture on 29.03.2007)
7 V. Hlaváč 12.04.2007 Linear classifiers and its learning.
8 V. Hlaváč 19.04.2007  Support vector machines classifiers (SVM). Kernel methods.
9 V. Hlaváč 26.04.2007  Participation at the One-day colloquium of the CMP (9:00 - 16:00)
10 V. Hlaváč 3.05.2007  Unsupervised learning. Cluster analysis. EM (Expectation Maximization) algorithm.
11  V. Hlaváč  10.05.2007  Recognition of Markovian sequences.
12  V. Hlaváč

17.05.2007

Graphical models.
    24.05.2007 No lecture because V. Hlaváč will be on a business trip to Japan.
13 V. Hlaváč 31.05.2007 Feature ranking and selection. Experimental evaluation of the classifiers. Receiver operator curve (ROC).
14 V. Hlaváč 7.06.2007 Relation between statistical and structural pattern recognition.

Recommended literature:

  1. Schlesinger M.I., Hlaváč V.: Ten lectures on statistical and structural pattern recognition, Kluwer Academic Publishers, Dordrecht, The Netherlands, 2002. Edition in Czech Vydavatelství ČVUT Praha, 1999.
  2. Duda R.O., Hart, P.E.,Stork, D.G.: Pattern Classification, John Willey and sons, 2nd edition, New York, 2001.
  3. Bishop, C.M: Pattern Recognition and Machine Learning, Springer, New York, 2006.

Students of the subject:

Presentations (It is likely that presentation will be updated before the lecture):

Poslední změna 26.02. 2007 V. Hlaváč, hlavac@fel.cvut.cz