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