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Last update: doc. RNDr. Martin Branda, Ph.D. (11.12.2020)
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Last update: RNDr. Jitka Zichová, Dr. (06.05.2021)
An introduction to basic machine learning principles and its use in practice. |
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Last update: RNDr. Václav Kozmík, Ph.D. (09.02.2022)
Details can be found on the webpage: https://www2.karlin.mff.cuni.cz/~kozmikk/DS2.php |
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Last update: RNDr. Václav Kozmík, Ph.D. (11.12.2020)
Yoshua Bengio, Ian Goodfellow, Aaron Courville: Deep learning, MIT Press, In preparation. Jürgen Schmidhuber: Deep learning in neural networks: An overview, Neural networks 61 (2015): 85-117. Friedman, J. H. (March 1999): Stochastic Gradient Boosting, Computational Statistics and Data Analysis, vol. 38, pp. 367-378 |
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Last update: RNDr. Jitka Zichová, Dr. (06.05.2021)
Lecture + exercises. |
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Last update: RNDr. Václav Kozmík, Ph.D. (21.04.2022)
Exam will include solving a practical task in Python with discussion about selected algorithm, its theoretial background and results achived in the practical task. Student will receive a data set together with a description of the prediction task which needs to be solved. |
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Last update: RNDr. Václav Kozmík, Ph.D. (11.12.2020)
Lectures: • introduction to machine learning, motivation, examples • general methods in machine learning: split of dataset to training and validation, over-fitting, regularization • methods using decision trees: decision trees, random forest, gradient boosting • methods using neural networks: simple neural networks, convolutional neural networks, recurrent neural networks • clustering methods – supervised vs unsupervised • other classification methods – support vector machine, naive Bayes
Practicals: • Practicals will be held in computer lab and Python language will be used • Machine learning algorithms will be applied on real data |
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Last update: doc. Ing. Marek Omelka, Ph.D. (19.11.2021)
Necessary:
Good to know:
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