Lectures

	Date	Lecturer	Contents	Materials
1	22.2.	Drchal	Artificial Neural Networks – history, typical problems solved by ANNs, learning algorithms, perceptron	a4m33bia-02ann_intro-2016.pdf Mathematica notebook
2	29.2.	Drchal	MultiLayer perceptron (MLP), Radial Basis Function (RBF) & Group Method of Data Handling (GMDH)	a4m33bia-02mlp_rbf_gmdh-2016.pdf
3	7.3.	Drchal	Backpropagation in detail & Deep Neural Networks (DNNs)	a4m33bia-03backprop-2016.pdf
4	14.3.	Kubalík	Standard genetic algorithm – evolutionary cycle, genetic operators, schema theorem	a4m33bia_sga_2016.pdf
5	21.3.	Kubalík	Genetic programming – basic principles, applications	a4m33bia_geneticprogramming_2015.pdf
6	28.3.		Easter
7	4.4.	Drchal	Time series processing, Recurrent Neural Networks (RNN), Jordan/Elman network, BPTT, RTRL, Echo State Networks, LSTM	a4m33bia-06recurrent-2016.pdf
8	11.4.	Drchal	Unsupervised learning, Self-Organizing Map (SOM)	a4m33bia-04som-2016.pdf
9	18.4.	Kubalík	Multiobjective optimization – dominance, Pareto-optimal solutions, NSGA-II, SPEA2	a4m33bia_moea_2015.pdf
10	25.4.	Kubalík	Evolutionary algorithms with real representation – Evolution strategy, crossover operators, differential evolution	a4m33bia_realcodedea_2016.pdf
11	2.5.	Kubalík	Evolutionary algorithms for dynamic optimization	a4m33bia_eafordynamicoptimization_2015.pdf
12	9.5.	Kubalík	Ant colony optimization, Particle swarm optimization	a4m33bia_aco_pso_2016.pdf
13	16.5.	Drchal	Neuroevolution. NEAT. Direct and indirect encoding of neural networks. Cellular Encoding and HyperNEAT	a4m33bia-13neuroevolution-2016.pdf a4m33bia-13indirect-2016.pdf
14	23.5.	Kubalík / Drchal	Reserve

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