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Introduction of modern computational intelligence methods (evolutionary algorithms, machine learning and
related fields) and their application to solving of real problems. Basic knowledge of machine learning, neural
networks and evolutionary algorithms is required.
Last update: T_KTI (05.05.2015)
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Teach advanced methods combining evolutionary algorithms, neural networks, and other computational intelligence methods. Deepen the knowledge from the introductory courses on neural networks, machine learning, data mining and evolutionary algorithms. The seminar will be focused on working with real data and the cooperation of various methods while solving difficult problems from the areas of optimization, learning and modelling. Last update: T_KTI (05.05.2015)
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In order to pass the course, the student must obtain the credit, which is given for a presentation of group project created as part of the seminar. Last update: Pilát Martin, doc. Mgr., Ph.D. (13.10.2017)
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[1] Trevor Hastie, Robert Tibshirani, Jerome Friedman. The elements of statistical learning. Vol. 2, no. 1., Springer, 2009.
[2] Alex A. Freitas. Data Mining and Knowledge Discovery with Evolutionary Algorithms, Springer, 2002.
[3] Yoshua Bengio, Ian J. Goodfellow, and Aaron Courville. Deep Learning. MIT Press, 2015 (in print), [online: http://www.iro.umontreal.ca/~bengioy/dlbook]
[4] Charu C. Aggarwal: Data Mining - The Textbook. Springer 2015, ISBN 978-3-319-14141-1
[5] Thomas Bäck, Christophe Foussette, and Peter Krause. Contemporary Evolution Strategies. Springer Science & Business Media, 2013.
[6] P. Brazdil, C. Giraud Carrier, C. Soares, R. Vilalta: Metalearning. Springer, 2009. Last update: T_KTI (05.05.2015)
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The subject aims at deepening of the knowledge from the following areas with the focus on their application to real data from different competitions (e.g. Kaggle, conference competitions, …).
Metalearning - model selection, hyper-parameter tuning (grid search, evolutionary algorithms), ensembles (bagging, boosting, stacking, blending)
Combination of evolutionary algorithms and machine learning - surrogate modelling; hybrid models, relation between local and global search, memetic algorithms; evolution and metalearning
Advanced evolutionary computing - CMA-ES, constrained optimization
Kernel methods - support vector machines (classification, regression), kernel neural networks, Radial Basis Function Networks
Semi-supervised learning - self-learning, generative models, Semi-Supervised Support Vector Machines, graph-based methods
Advanced models of neural networks - Echo State Network, Long Short Term Memory Network, autoencoders, convolution networks, Boltzmann machines, deep networks Last update: T_KTI (05.05.2015)
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