XP33ROD – Rozpoznávání pro doktorandy (Pattern Recognition)

  • Lectures timetable. There are small modifications of the timetable on several dates. See the actual state of the timetable in a table below. yet.

Introduction, subject goals, eligibility

The subject introduces pattern recognition (called also machine learning). If there is one non-Czech speaking student in the audience, the lecture wil be in English. The subject is intended for doctoral student. I can take master students after an initial interview about student's motivation as well. The lectures are open to bachelor students but no credit for the subject will be awarded.

It is expected that student did not study this subject before, e.g. in the bachelor subject Rozpoznávání a strojové učení, B4M33RPZ, B4M33RPZ lectured by Prof. Jiří Matas. The lecturer will ask the student if she/he did not pass a similar subject to avoid getting credits easily.

The subject comprises of lectures and consultations with the lecturer (if the student asks for it).

Expected preliminary knowledge

It is required that the student passed an introductory course of the probability theory and statistics or studied this area individually.


  • 2 x 45 minutes of lecture a week, i.e. 90 minutes in one single lecture.
  • Lectures take place in the room JP:B-633, building B, Praha 6, Jugoslávských partyzánů 1580/3. The lectures are scheduled regularly for Tuesdays from 15:15 to 16:45. However, the irregularites might be needed due to the lecturers' business trips. The replacement date will be discussed with the students.
  • If there is one non-Czech speaking student in the audience then the lecture will be given in English. The lecture presentation will be in English typically even if the lecture is given in Czech.
  • Presentations to be given/given at the lecture are available either in English or in Czech or in both languages. The presentation will be typically improved before the lectutre.
  • Lecturer's abbreviation VH - Václav Hlaváč, RŠ - Radoslav Škoviera
Week Date Time Content
0119.02.201916:00 VH Rehearsal of the basic knowledge from probability theory and statistics. Outline of pattern recognition.
0226.02.201915:15 VH Bayesian task and its two special cases. Non-Bayesian tasks, formulations only. Conditional independence of features. Gaussian models.
0305.03.201915:15 Artificial neural networks.
0412.03.201915:15 VH Data normalization. Experimental evaluation of classifiers. Receiver operator curve (ROC).
19.03 No lecture. Both VH and RŠ travel this week.
0526.03.201915:15 VH Estimation of probabilistic models. Parametric and nonparametric methods.
0602.04.201915:15 Convolutional neural networks.
0704.04.201910:00 VH Learning in pattern recognition. (Replacement lecture)
0809.04.201915:15 VH Linear classifiers. Perceptron. Learning (training) algorithm.
0916.04.201915:15 VH VC dimension and its use. Support vector machines classifiers (SVM). Kernel methods. Training algorithms for SVM.
10no lecture15:15 VH
1130.04.201915:15 VH Unsupervised learning. Cluster analysis. K-means algorithm.
1207.05.201915:15 VH Unsupervised learning. EM algorithm.
1314.05.201915:15 Reinforcement learning.
1421.05.201915:15 VH Markovian models in pattern recognition.

Examination and its evaluation

  • The exam has two parts, a written test and an oral part. The written test checks the students overview in the subject. In a written part, the student answers typically five questions. The written test counts for 50 points maximally.
  • In the oral part of the exam following the written part, the teacher finds a topic related to students paper (see the individual work above) and related to the subject. The aim of the oral exam is to check if the student knows some part of the subject topic in more detail. The oral part counts for 50 points maximally.
  • The resulting mark follows from the sum of points. The maximal number of points is 100. The evaluation Excellent is for 80 points and more. The evaluation Passed is for 51 points and more. Otherwise the student failed at the exam.


  • 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.
  • Duda R.O., Hart, P.E., Stork, D.G.: Pattern Classification, John Willey and sons, 2nd edition, New York, 2001.
  • Bishop, C.M: Pattern Recognition and Machine Learning, Springer, New York, 2006.

Students of the subject in the summer semester 2018/2019

Registered students:

  1. Lukáš Forst, 3rd year of bachelor study Open Informatics FEL
  2. Jan Levínský, 1st year of bachelor study Open Informatics FEL
  3. Tomáš Mazel, 3rd year of bachelor study Web and software engineering FIT
  4. Matěj Petrlík, doctoral student, Artificial Intelligence and Bioinformatics, FEL
  5. Tomáš Šabata, doctoral student, Informatics, FIT
  6. Anežka Štěpánková, 2nd year master studies System Programming, FIT
  7. Mariana Tavares Pimenta, visiting student
  8. Matej Uhrín, doctoral student, Informatics and computer engineering, FEL
  9. Matouš Vrba, doctoral student Artificial Intelligence and Bioinformatics, FEL
  10. Qingxiang Wang, doctoral student Artificial Intelligence and Bioinformatics, FEL (will be on an internship at Amazon in USA, returns on April 5, 2019)

Students which have to register:

Extraordinary students

Megumi Miyashita (a visiting student from TUAT Tokyo, only till the end of February 2019)

courses/xp33rod/start.txt · Last modified: 2019/04/16 16:41 by hlavac