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CZECH TECHNICAL UNIVERSITY IN PRAGUE
STUDY PLANS
2023/2024
UPOZORNĚNÍ: Jsou dostupné studijní plány pro následující akademický rok.

Data Analysis and Computational Intelligence

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Code Completion Credits Range
W31OZ001 ZK 39P+39C
Garant předmětu:
Ivo Bukovský, Michael Valášek
Lecturer:
Václav Bauma, Ivo Bukovský, Zbyněk Šika, Michael Valášek
Tutor:
Václav Bauma, Ivo Bukovský, Zbyněk Šika, Michael Valášek
Supervisor:
Department of Mechanics, Biomechanics and Mechatronics
Synopsis:

Doctoral students gain an overview of computational intelligence and data analysis methods and can orientate

themselves in the classical and latest trends in artificial intelligence using machine learning and neural networks. Deeper insights and skills will be gained by preparing the subject project for a given topic according to the particular doctoral candidate or study group.

•History of computational and artificial intelligence, data analytics and datamining

•Principles of machine learning, adaptation algorithms, optimization

•Principle of supervised neural networks for prediction and classification

•Basic methods of data analysis (linear and nonlinear correlation analysis, MI, PCA, SVD)

•Methods of clustering and N-dimensional data visualization methods (k-means, PCA, SVD, tSNE)

•Self-organizing maps

•Fundamentals of large data processing methods and tools (BigData, k-medoids, Hadoop, MapReduce, Spark)

•Autoencoders for classification and reduction of dimensionality

•Detection of unexpected states by neural networks

•New methods of computational intelligence for data processing (DeepLearning, Transfer Learning, Transformer Neural Networks, ...)

Requirements:
Syllabus of lectures:
Syllabus of tutorials:
Study Objective:
Study materials:

•DAVID B FOGEL, DERONG LIU a JAMES M KELLER. Fundamentals of Computational Intelligence: Neural Networks, Fuzzy Systems, and Evolutionary Computation. Piscataway, NJ: IEEE Press, 2016. ISBN 978-1-119-21434-2.

•RAMSUNDAR, Bharath a Reza Bosagh ZADEH. TensorFlow for deep learning: from linear regression to

reinforcement learning. 2018. ISBN 978-1-4919-8042-2.

•LECUN, Yann, Yoshua BENGIO a Geoffrey HINTON. Deep learning. Nature. 2015, 521(7553), 436–444.

•BUKOVSKY, Ivo, Witold KINSNER a Noriyasu HOMMA. Learning Entropy as a Learning-Based Information Concept. Entropy. 2019, 21(2), 166. ISSN 1099-4300. Dostupné z: doi:10.3390/e21020166

Note:
Time-table for winter semester 2023/2024:
Time-table is not available yet
Time-table for summer semester 2023/2024:
Time-table is not available yet
The course is a part of the following study plans:
Data valid to 2024-03-27
Aktualizace výše uvedených informací naleznete na adrese https://bilakniha.cvut.cz/en/predmet6688106.html