The subject introduces pattern recognition (called also machine learning). The subject is intended for doctoral students. 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).
It is required that the student passed an introductory course of the probability theory and statistics or studied this area individually.
Week | Date | Time | Who | Content |
---|---|---|---|---|
01 | 18.02.2020 | 15:15 | VH | Rehearsal of the basic knowledge from probability theory and statistics. Probability En, Probability Cz |
02 | 25.02.2020 | 15:15 | VH | Outline of pattern recognition. Bayesian task and its two special cases. Intro to pattern recognition, Bayesian task |
xx | 03.03.2020 | VH | No lecture. V. Hlaváč travels to a business trip. | |
xx | 10.03.2020 | VH | No lecture due to COVID-19 crisis. The lectures will distant using Microsoft Teams tool until further notice. | |
03 | 17.03.2020 | 15:15 | VH | Non-Bayesian tasks, formulations only. Conditional independence of features. Gaussian models. Non-bayesian tasks, Two statistical models. |
04 | 24.03.2020 | 15:15 | VH | Estimation of probabilistic models. Parametric and nonparametric methods. parametric, nonparametric |
05 | 31.03.2020 | 15:15 | RŠ | Artificial neural networks. slides |
06 | 07.04.2020 | 15:15 | RŠ | Convolutional neural networks. slides |
07 | 14.04.2020 | 15:15 | VH | Data normalization. Experimental evaluation of classifiers. Receiver operator curve (ROC). Experimental classifier performance evaluation |
08 | 21.04.2020 | 15:15 | VH | Learning in pattern recognition. VC dimension and its use. Learning in Pattern Recognition, four formulations, VC learning theory |
09 | 28.04.2020 | 15:15 | VH | Linear classifiers. Perceptron. Learning (training) algorithm. Linear classifiers. Perceptron. |
10 | 05.05.2019 | 15:15 | VH | Support vector machines classifiers (SVM). Kernel methods. Training algorithms for SVM Linear SVM , Kernel SVMs |
11 | 12.05.2020 | 15:15 | VH | Unsupervised learning. K-means algorithm. EM algorithm. Unsupervised learning |
12 | 19.05.2020 | 15:15 | VH | Markovian models in pattern recognition. Markovian pattern recognition methods |
13 | 26.05.2020 | 15:15 | RŠ | Reinforcement learning. slides |
The list of journals from which the paper can be selected: IEEE Transactions on Pattern Analysis and Machine Intelligence, Pattern Recognition. The Czech Government pays to its universities the electronic access to papers. Use https://dialog.cvut.cz/
Registered students:
Students who have to register:
Extraordinary students