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Lectures

No.	Date	Lecturer	Contents	Materials
1	17.2.	Kubalík	Introduction to optimization – standard constructive and generative optimization approaches	a4m33bia-01intro.pdf
2	24.2.	Drchal	Artificial neural networks – history, typical problems solved by ANN, learning algorithms, perceptron	a4m33bia-02ann_intro-2013.pdf a4m33bia-02ann_mathematica_notebook.zip
3	3.3.	Drchal	Multilayer neural networks – back-propagation learning lagorithm	a4m33bia-03backprop-2013.pdf
4	10.3.	Kubalík	Standard genetic algorithm – evolutionary cycle, genetic operators, schema theorem	a4m33bia_sga_2011b.pdf
5	17.3.	Kubalík	Genetic programming – basic principles, applications	a4m33bia_geneticprogramming_2011.pdf
6	24.3.	Drchal	Time series processing, recurrent neural networks, Jordan/Elman network, BPTT, RTRL, Echo State Networks, LSTM. Introduction to neuroevolution – evolutionary optimization of the neural network's structure and weights.	a4m33bia-13recurrent-2013.pdf recurrent_recall_demo.nb.zip
7	31.3.	Kubalík	Multiobjective optimization – dominance, Pareto-optimal solutions, NSGA-II, SPEA2	a4m33bia_moea_2012.pdf
8	7.4.	Kubalík	Evolutionary algorithms with real representation – Evolution strategy, crossover operators, differential evolution	a4m33bia_realcodedea_2012.pdf
9	14.4.	Drchal	Neuroevolution. NEAT. Direct and indirect encoding of neural networks. Cellular Encoding a HyperNEAT.	a4m33bia-10neuroevolution-2013.pdf a4m33bia-14indirect.pdf
10	21.4.		Velikonoce
11	8.4.	Drchal	Univerzal approximation, Kolmogorov theorem, local and global units, RBF nets, GMDH nets	a4m33bia-05rbf_gmdh-2013.pdf
12	5.5.	Drchal	Unsupervised learning – clustering, selforganization, Kohonen self-organizing map	a4m33bia-04som-2013.pdf
13	12.5.	Kubalík	Evolutionary algorithms for dynamic optimization	a4m33bia_eafordynamicoptimization_2013.pdf
14	19.5.	Kubalík	Ant colony optimization, Particle swarm optimization	a4m33bia_aco_pso_2013.pdf

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