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Praktický úvod do strojového učení

Summary

During the course, the participant will learn basic methods for data analysis, machine learning and regression analysis using the widely used Pandas, Scikit-learn and PyTorch libraries in a Python environment. Graduates of the course will be able to process large-scale data using machine learning and perform data classification using tools designed to apply neural networks.


Literature

NORGAARD, M. Neural networks for modelling and control of dynamic systems: a practitioner's handbook. Advanced textbooks in control and signal processing. London: Springer, c2000. ISBN 1-85233-227-1.

Advised literature

JHA, A.R. Mastering PyTorch: Build powerful neural network architectures using advanced PyTorch 1. x features. Packt Publishing Ltd, 2021.
MCKINNEY, Wes. Python for data analysis: Data wrangling with Pandas, NumPy, and IPython." O'Reilly Media, Inc.", 2012.

https://pytorch.org/tutorials/
https://pandas.pydata.org
https://www.tensorflow.org/learn


Language of instruction čeština
Code 653-2248
Abbreviation PUSU
Course title Praktický úvod do strojového učení
Coordinating department Department of Materials Engineering and Recycling
Course coordinator Ing. Lukáš Halagačka, Ph.D.