FEKT-MPA-MLRAcad. year: 2020/2021
Learning outcomes of the course unit
Recommended optional programme components
Recommended or required reading
N. Buduma: Fundamentals of Deep Learning, O'Reilly Media, 2017 (CS)
Ch. M. Bishop: Pattern Recognition and Machine Learning, Springer, 2011
I. Goodfellow, Y. Bengio, A. Courville, F. Bach: Deep Learning, The MIT Press, 2016
Planned learning activities and teaching methods
Assesment methods and criteria linked to learning outcomes
Language of instruction
1. Introduction to classification. Classification error, classifier testing.
2. Linear Classifiers - basic principles and methods.
3. Bayesian approach to classification. Naive Bayes classifier.
4. Selected types of neural networks: Kohonen, Hopfield and Boltzman.
5. Classification by support vector machine method.
6. Decision and regression trees and forests, random forests.
7. Methods for improving classifier properties (bagging, boosting).
8. Advanced methods of feature selection and visualization (mRMR, t-SNE).
9. Principles of deep learning, deep neural networks (NN) and basic building blocks.
10. Principles of deep NN learning. Hardware requirements.
11. Variants of deep NN, autoencoders, recurrent NN.
12. Application of classification tasks for processing of signals, images and bioinformatic data. Application examples.
13. Implementation packages for deep learning (Caffe, TensorFlow, Statistics and Machine Learning Toolbox for Matlab and others).
Classification of course in study plans
- Programme MPA-BIO Master's, 2. year of study, winter semester, 5 credits, compulsory