Teaching materials for the seminar on Application of Computational Intelligence Methods
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00Utility Pridani prikladu na pouziti interaktivniho grafu v Ipython Notebooku. Jan 12, 2016
01Vizualizace First lesson Oct 8, 2015
02Priznaky
03Redukce Pridano povidani o redukci dat a vyberu priznaku Oct 21, 2015
04LinearniModely Pridany rekuretni site a semi-supervised learning Dec 9, 2015
05Kernely Add files via upload Sep 10, 2016
06DeepNet
07RekurentniSite Opravdu pridany rekuretni site a semi-supervised learning. Dec 9, 2015
08Ensembles Add files via upload Sep 9, 2016
09SemisupervisedUceni Opravdu pridany rekuretni site a semi-supervised learning. Dec 9, 2015
10MetaLearning Add files via upload Sep 10, 2016
11NahradniModely Pridany nahradni modely v evolucnich algoritmech Jan 5, 2016
.gitignore
README.md Opraveny odkazy v README. Dec 10, 2018

README.md

Application of Computational Intelligence Methods

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.

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.

Additional study resources/papers

Deep Style:

Fooling Neural Networks:

Neural Machine Translation:

Image Super-Resolution:

Deep reinforcement learning:

AlphaGO:

Generative Adversarial Networks

Model calibration