The subject introduces pattern recognition (called also machine learning).

It is expected that student did not study this subject before, e.g. in the bachelor subject Rozpoznávání a strojové učení, A4M33RPZ lectured by Prof. Jiří Matas. The lecturer will ensure that a similar subject is not taken for the second time to get credits easily.

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

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

- 2 hours of lecture a week, i.e. 90 minutes.
- Lectures take place in the room KN:G-102a, building G, Karlovo náměstí campus of ČVUT. The lectures are scheduled regularly for Wednesdays from 12:45 to 14:15. However, there can be changes needed due to the lecturer's business trips. The replacement date will be discussed with the students in advance.
- 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.

Week | Date | Content |
---|---|---|

1 | 02.03.2016 | Rehearsal of the basic knowledge from probability theory and statistics. Outline of pattern recognition. |

2 | 09.03.2016 | Bayesian task and its two special cases. |

- | 16.03.2016 | No lecture. VH on a business trip. |

3 | 23.03.2016 | Non-Bayesian tasks, formulations only. Conditional independence of features. Gaussian models. Feature space straightening. |

4 | 30.03.2016 | Data normalization. Experimental evaluation of classifiers. Receiver operator curve (ROC). |

5 | 06.04.2016 | Estimation of probabilistic models. Parametric methods. |

6 | 13.04.2016 | Estimation of probabilistic models. Nonparametric methods. |

7 | 20.04.2016 | Learning in pattern recognition. |

8 | 27.04.2016 | VC dimension. Estimate of the needed length of the training sequence. |

9 | 04.05.2016 | Linear classifiers and their learning. Support vector machines classifiers (SVM). Kernel methods. |

10 | 11.05.2016 | No lecture. Rector's day. |

11 | 18.05.2016 | Vít Listík: Deep convolutional networks. |

12 | 25.05.2016 | Unsupervised learning. Cluster analysis. K-means algorithm, its relation to data compression. |

~~===== Individual work =====~~

~~The student has to write a text in the form of a paper (research report). Ideally this text is part of student's research and relates to the subject. The aim of writing the paper is to teach help her/him improving the skill of paper writting and outlining the domain for the oral examination. It is preferred if this paper is typeset in LaTeX. The student is welcome to consult this paper with the lecturer during the semester.~~

- 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 81 points and more. The evaluation Passed is for 55 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.

Jana Ahmed, Jakub Kákona, Vít Listík, Miroslav Uller, Ibrahim Aboukashabah

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