Machine Learning - NAIL029
Title: Strojové učení
Guaranteed by: Department of Theoretical Computer Science and Mathematical Logic (32-KTIML)
Faculty: Faculty of Mathematics and Physics
Actual: from 2015
Semester: summer
E-Credits: 3
Hours per week, examination: summer s.:2/0, Ex [HT]
Capacity: unlimited
Min. number of students: unlimited
4EU+: no
Virtual mobility / capacity: no
State of the course: taught
Language: Czech, English
Teaching methods: full-time
Guarantor: Mgr. Marta Vomlelová, Ph.D.
Class: Informatika Mgr. - Teoretická informatika
Informatika Mgr. - Matematická lingvistika
Classification: Informatics > Theoretical Computer Science
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Annotation -
The aim of the course is to introduce machine learning as important and in this time very vital field developing in the close connection with artificial intelligence. The course gives a survey of basic branches of machine learning (supervised inductive learning, reinforcement learning, unsupervised learning and knowledge in learning), main problems and methods and some typical algorithms.
Last update: T_KTI (03.05.2012)
Aim of the course -

The course extends the basic machine learning course.

Last update: Vomlelová Marta, Mgr., Ph.D. (14.05.2021)
Course completion requirements -

The exam consists of a written preparation and an oral part. The requirements are given by the course syllabus.

Last update: Vomlelová Marta, Mgr., Ph.D. (07.06.2019)
Literature -

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.

Last update: Vomlelová Marta, Mgr., Ph.D. (11.05.2023)
Requirements to the exam -

The exam consists of a written preparation and an oral part. The requirements are given by the course syllabus.

Last update: Vomlelová Marta, Mgr., Ph.D. (07.06.2019)
Syllabus -

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,

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.

Last update: Vomlelová Marta, Mgr., Ph.D. (21.05.2025)