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Last update: RNDr. Jan Hric (27.04.2018)
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Last update: RNDr. Jan Hric (27.04.2018)
Introduce the basics of nature-inspired algorithms used in machine learning and optimization (evolutionary algorithms, neural networks, etc.). |
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Last update: RNDr. Jan Hric (27.04.2018)
In order to pass the course, the student must obtain the credit for the seminar and pass an exam. The credit is given for solving assignments from the seminar. The nature of study verification excludes the possibility of its repetition. |
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Last update: RNDr. Jan Hric (27.04.2018)
1. Olarius S., Zomaya A. Y., Handbook of Bioinspired Algorithms and Applications, Chapman & Hall/CRC, 2005. ISBN: 978-1-584-88475-0
2. de Castro, L. N., Fundamentals of Natural Computing: Basic Concepts, Algorithms, and Applications, CRC Press, 2006. ISBN: 978-1-584-88643-3
3. Eiben, A.E and Smith, J.E.: Introduction to Evolutionary Computing, (2nd ed), Springer-Verlag, 2015. ISBN: 978-3-662-44874-8
4. Poli R., Langdon W. B., McPhee, N. F., A Field Guide to Genetic Programming. Lulu.com, 2008 ISBN: 978-1-409-20073-4
5. Bengio Y., Goodfellow I. J., Courville A., Deep Learning. MIT Press, 2016. ISBN: 978-0-262-03561-3 |
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Last update: RNDr. Jan Hric (27.04.2018)
The exam is oral with time for written preparation. The requirements correspond to the syllabus in the extent presented during the lectures. A part of the exam may be the design of an algorithm for a given problem.
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Last update: RNDr. Jan Hric (27.04.2018)
a. evolutionary models b. neural models
a. Simple genetic algorithm b. Representation, genetic operators, fitness, selection c. Evolutionary algorithms for continuous optimization d. Neuro-evolution, algorithm NEAT e. Genetic programming
a. Ant Colony Optimization b. Particle Swarm Optimization
a. Perceptron, multi-layered perceptron, back-propagation as a learning algorithm b. Convolutional networks c. RBF networks a Kohonen’s maps
a. Artificial Immune Systems b. Cellular Automata c. Artificial Life
a. Continuous and combinatorial optimization b. Multi-objective optimization c. Supervised and unsupervised learning, reinforcement learning |