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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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courses/a4m33bia/lectures.txt · Last modified: 2016/05/16 14:14 by drchajan