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Last update: Mgr. Dalibor Šmíd, Ph.D. (13.05.2022)
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Last update: Hong Van Le, Ph.D. (12.09.2021)
1. Getting involved is a prerequisite for participate in the exam.
2. Questions in the exam correspond to the syllabus of the subject to the extent it was presented at the lecture. Alternatively, students can choose a term paper assignment.
3. The final mark takes account for an active participation in the lecture. |
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Last update: Hong Van Le, Ph.D. (12.09.2021)
1. S. Shalev-Shwart and S. Ben-David, Understanding Machine Learning: From Theory to Algorithms, Cambridge University Press, 2014.
2. M. Mohri, A. Rostamizadeh, A. Talwalkar, Foundations of Machine Learning,MIT Press, second Edition, 2018.
3. L. Deveroye, L. Gy\"orfi and G. Lugosi, A Probabilistic Theory of Pattern Recognition, Springer 1996.
4. Lecture notes ``Mathematical foundations of machine learning"
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Last update: Hong Van Le, Ph.D. (12.09.2021)
1. Statistical models of machine learning. 2. Supervised learning, unsupervised learning. 3. Generalization ability of machine learning. 4. Neural networks and deep learning. 5. Bayesian machine learning and Bayesian networks. |