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, B4M33RPZ, B4M33RPZ 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.
|01||21.02.2018||09:00||Rehearsal of the basic knowledge from probability theory and statistics. Outline of pattern recognition.|
|02||28.02.2018||09:00||Bayesian task and its two special cases.|
|03||07.03.2018||09:00||Non-Bayesian tasks, formulations only. Conditional independence of features. Gaussian models. Feature space straightening.|
|04||07.03.2018||11:00||Data normalization. Experimental evaluation of classifiers. Receiver operator curve (ROC).|
|-||14.03.2018||-||No lecture because of V. Hlaváč's busineess trip.|
|-||21.03.2018||-||No lecture because of V. Hlaváč's busineess trip.|
|05||28.03.2018||09:00||Estimation of probabilistic models. Parametric methods.|
|06||28.03.2018||11:00||Estimation of probabilistic models. Nonparametric methods.|
|07||04.04.2018||09:00||Learning in pattern recognition.|
|08||11.04.2018||09:00||Linear classifiers. Perceptron. Learning (training) algorithm.|
|09||18.04.2018||09:00||VC dimension and its use. Support vector machines classifiers (SVM).|
|10||25.04.2018||09:00||Training algorithms for SVM. Kernel methods.|
|11||02.05.2018||09:00||Unsupervised learning. Cluster analysis. K-means algorithm.|
|12||09.05.2018||09:00||Unsupervised learning. EM algorithm.|
|13||09.05.2018||11:00||Markovian models in pattern recognition.|
|14||16.05.2018||09:00||Neural networks. Backpropagation. Convolutional networks.|
The student is supposed to develop an own scientifc paper written in English related to the subject domain and (possibly) to her/his own research. The topic is suggested by the student and approved by the teacher. The paper will be in English. The paper draft has to have 8 “Springer proceedings” pages. The paper will be developed in a tool observable/editable by the teacher a.s. Gitlab/LaTeX or Google Docs. The paper draft will be consulted two times with the teacher in the week 6, week 11 at latest. The final version of the paper ha to be delivered in print to the teacher at week 11. The marked version by the teacher on this print will be given back to the student at the week 13.
Zagroz Abdulkhaliq Aziz (firstname.lastname@example.org), Maria Rigaki (email@example.com), Kiriaki-Maria Saiti (firstname.lastname@example.org), Gabriela Šejnová (email@example.com), Petr Švarný (firstname.lastname@example.org), Michael Tesař (email@example.com)
Students which have to register:
Jan Hauser (firstname.lastname@example.org), Václav Mácha (email@example.com),
Extraordinary students (visiting students working for 4 months with V. Hlaváč) Richard Lengyel (Hungary), Matthias De Ryck (Belgium)