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--- courses:ae4m33bia:lectures [2014/02/16 14:46]
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+++ courses:ae4m33bia:lectures [2015/03/08 20:59]
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 ====== Lectures ======
-^ No.  ^ Date  ^ Lecturer  ^ Contents ^ Materials ^
-| 1  | 17.2. | Kubalík | Introduction to optimization – standard constructive and generative optimization approaches   | {{:courses:a4m33bia:a4m33bia-01intro.pdf|}} |
-| 2  | 24.2. | Drchal  |Artificial neural networks – history, typical problems solved by ANN, learning algorithms, perceptron   | {{:courses:a4m33bia:a4m33bia-02ann_intro-2013.pdf|}} {{:courses:a4m33bia:a4m33bia-02ann_mathematica_notebook.zip|}} |
-| 3  | 3.3. | Drchal | Multilayer neural networks – back-propagation learning lagorithm | {{:courses:a4m33bia:a4m33bia-03backprop-2013.pdf|}} |
-| 4  | 10.3. | Kubalík | Standard genetic algorithm – evolutionary cycle, genetic operators, schema theorem   | {{:courses:a4m33bia:a4m33bia_sga_2011b.pdf|}} |
-| 5  | 17.3. | Kubalík | Genetic programming – basic principles, applications  | {{:courses:a4m33bia: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. | {{:courses:ae4m33bia:a4m33bia-13recurrent-2013.pdf|}} {{:courses:a4m33bia:recurrent_recall_demo.nb.zip|}}  |
-| 7  | 31.3. | Kubalík | Multiobjective optimization – dominance, Pareto-optimal solutions, NSGA-II, SPEA2  | {{:courses:a4m33bia:a4m33bia_moea_2012.pdf|}}  |
-| 8  | 7.4. | Kubalík | Evolutionary algorithms with real representation – Evolution strategy, crossover operators, differential evolution   | {{:courses:a4m33bia:a4m33bia_realcodedea_2012.pdf|}} |
-| 9  | 14.4. | Drchal | Neuroevolution. NEAT. Direct and indirect encoding of neural networks. Cellular Encoding a HyperNEAT.  | {{:courses:a4m33bia:a4m33bia-10neuroevolution-2013.pdf|}} {{:courses:a4m33bia:a4m33bia-14indirect.pdf|}}  |
-| 10  | 21.4.  |  | Easter  |  |
-| 11  | 8.4.  | Drchal | Univerzal approximation, Kolmogorov theorem, local and global units, RBF nets, GMDH nets  | {{:courses:a4m33bia:a4m33bia-05rbf_gmdh-2013.pdf|}}|
-| 12  | 5.5.  | Drchal | Unsupervised learning – clustering, selforganization, Kohonen self-organizing map  | {{:courses:a4m33bia:a4m33bia-04som-2013.pdf|}}  |
-| 13  | 12.5.  | Kubalík | Evolutionary algorithms for dynamic optimization   | {{:courses:a4m33bia:a4m33bia_eafordynamicoptimization_2013.pdf|}} |
-| 14  | 19.5.  | Kubalík | Ant colony optimization, Particle swarm optimization  | {{:courses:a4m33bia:a4m33bia_aco_pso_2013.pdf|}}  |
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