Data Analysis and Computational Intelligence

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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, ...)

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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), 436444.

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:

Further information:

No time-table has been prepared for this course

The course is a part of the following study plans: