English

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The home faculty only

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A maximum of 30 points can be obtained during the semester. A maximum of 70 points can be obtained in the final examination.

There will be 6 tests during the semester (each test for maximum of 5 points). The tests cannot be repeated.

The conditions for the award of credit are as follows:
- full participation in the computer labs (max. two excused absences),
- obtaining at least 15 points from the tests.

Obtaining credit is a condition for admission to the final examination.

The final exam will be marked with a maximum of 70 points. A minimum of 35 points is required to pass the exam.

1. Introduction to classification. Classification error, classifier testing.
2. Features assessment, feature selection and feature reduction with basic and advanced methods (PCA, mRMR, t-SNE).
3. Linear Classifiers - basic principles and methods (perceptron, SVM, MSE).
4. Kernel approach for non-linear classification.
5. Bayesian approach to classification. Naive Bayes classifier.
6. Maximum likelihood and Maximum a-posteriori probability.
7. Decision and regression trees and forests, random forests.
8. Methods for improving classifier properties (bagging, boosting).
9. Basics of neural networks, regularization.
10. Principles of deep learning, deep neural networks (NN) and basic building blocks.
11. Principles of deep NN learning.
12. Variants of deep NN, autoencoders, recurrent NN, LSTM, GRU, GAN.
13. Application of classification tasks for processing of signals, images and bioinformatic data. Application examples.

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Deisenroth,M.P, Faisal, A.A, Ong, Ch.S.:Mathematics for Machine Learning, Cambridge University Press, 2020 (EN)
Geron, A.: Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems, 2. edition, O'Reilly Media (EN)
Ch. M. Bishop: Pattern Recognition and Machine Learning, Springer, 2011
I. Goodfellow, Y. Bengio, A. Courville, F. Bach: Deep Learning, The MIT Press, 2016
N. Buduma: Fundamentals of Deep Learning, O'Reilly Media, 2017 (CS)

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