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 | 17.02.2021 | 09:15 | VH | Outline of pattern recognition, Intro to pattern recognition |
02 | 24.02.2021 | 09:15 | VH | Rehearsal of the basic knowledge from probability theory and statistics. Probability En. Bayesian task and its two special cases, Bayesian task |
03 | 03.03.2021 | VH | Non-Bayesian tasks, formulations only. Conditional independence of features. Gaussian models. Non-bayesian tasks, Two statistical models. | |
04 | 10.03.2021 | VH | Estimation of probabilistic models. Parametric and nonparametric methods. parametric, nonparametric | |
05 | 17.03.2021 | 09:15 | RŠ | Artificial neural networks. slides |
06 | 24.03.2021 | 09:15 | RŠ | Convolutional neural networks. slides |
07 | 31.03.2021 | 09:15 | VH | Data normalization. Experimental evaluation of classifiers. Receiver operator curve (ROC). Experimental classifier performance evaluation |
08 | 07.04.2021 | 09:15 | VH | Learning in pattern recognition. VC dimension and its use. Learning in Pattern Recognition, four formulations, VC learning theory |
09 | 14.04.2021 | 09:15 | VH | Linear classifiers. Perceptron. Learning (training) algorithm. Linear classifiers. Perceptron. |
10 | 21.04.2021 | 09:15 | VH | Support vector machines classifiers (SVM). Kernel methods. Training algorithms for SVM Linear SVM , Kernel SVMs |
11 | 28.04.2021 | 09:15 | VH | Unsupervised learning. K-means algorithm. EM algorithm. Unsupervised learning |
12 | 05.05.2021 | 09:15 | VH | Markovian models in pattern recognition. Markovian pattern recognition methods |
13 | 12.05.2021 | 09:15 | RŠ | Reinforcement learning. slides |
14 | 19.05.2021 | 09:15 | VH | Structural pattern recognition |