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Last update: T_KTI (03.05.2012)
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Last update: Mgr. Marta Vomlelová, Ph.D. (14.05.2021)
The course extends the basic machine learning course. |
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Last update: Mgr. Marta Vomlelová, Ph.D. (07.06.2019)
The exam consists of a written preparation and an oral part. The requirements are given by the course syllabus. |
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Last update: Mgr. Marta Vomlelová, Ph.D. (11.05.2023)
T. Hastie, R. Tibshirani, J. Friedman: The Elements of Statistical Learning, Springer 2009
G. James, D. Witten, T. Hastie, R. Tibshirani: An Introduction to Statistical learning with Applications in R, Springer, 2014
S.J. Russell, P. Norvig: Artificial Intelligence: A Modern Approach; Prentice Hall, 1995
C. E. Rasmussen & C. K. I. Williams, Gaussian Processes for Machine Learning, the MIT Press, 2006
A. Cropper and S. Dumancic. Inductive logic programming at 30: a new introduction. CoRR, abs/2008.07912, 2020. |
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Last update: Mgr. Marta Vomlelová, Ph.D. (07.06.2019)
The exam consists of a written preparation and an oral part. The requirements are given by the course syllabus. |
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Last update: Mgr. Marta Vomlelová, Ph.D. (15.05.2024)
Linear regression and instance based learning as "extremal points" in the space of models,
basis expansion and regularization (smoothing splines and other methods),
logistic regression, generalized additive models,
model assessment (crossvalidation, one-leave-out, analytical criteria)
decision trees, prunning, missing values,
rule search PRIM,
model averaging, boosting, random forest,
Bayesian learning, EM algorithm introduced on an clustering example,
unsupervised learning - market basket analysis, clustering k-means, k-medoids, hierarchical clustering,
inductive logic programming,
Undirected graphical models, Gaussian processes and Bayesian optimization. |