University of Pardubice
Study programmes & Course catalogue
for academic year 2025/2026
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Course: Machine Learning

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Course title Machine Learning
Course code FES/HSU
Organizational form of instruction Lecture + Seminar
Level of course Doctoral
Year of study not specified
Semester Winter and summer
Number of ECTS credits 5
Language of instruction Czech
Status of course Optional
Form of instruction Face-to-face
Work placements This is not an internship
Recommended optional programme components None
Lecturer(s)
  • Hájek Petr, prof. Ing. Ph.D.
Course content
Theory of learning and selected learning tasks. Generative and discriminative algorithms. Support vector machines and support vector regression. Theoretical issues of training data selection. Feature selection and extraction. Ensemble of algorithms for supervised and unsupervised learning. Combining supervised and unsupervised learning. Analysis of learning outcomes and the problem of accuracy and complexity. Machine learning in information retrieval and knowledge extraction from text. Applications of machine learning in processing large volumes of data.

Learning activities and teaching methods
unspecified
Learning outcomes
The aim of the course is to familiarize doctoral students with the latest approaches in the field of machine learning. current trends in the development of machine learning algorithms for various types of learning tasks are discussed, as well as selected applications of machine learning, particularly in the processing of large data volumes.

Prerequisites
unspecified

Assessment methods and criteria
unspecified
As part of the course, students will prepare an independent paper of the scope required for a scientific journal article. The topic of this paper will relate to the doctoral student's dissertation and the methods used will include one of the contemporary approaches to machine learning. Examination: Oral, with a minimum pass rate of 60%.
Recommended literature
  • GOODFELLOW, I., BENGI, Y., COURVILLE, A. Deep learning. Cambridge: MIT Press, 2016.
  • GUYON, I., ELISSEEFF, A. An introduction to variable and feature selection. The Journal of Machine Learning Research, roč. 3, s. 1157-1182. 2003.
  • HASTIE, T., TIBSHIRANI, R., FRIEDMAN, J. The elements of statistical learning. Berlin: Springer., 2009.
  • CHAPELLE, O., SCHÖLKOPF, B., ZIEN, A. Semi-supervised learning. Cambridge: MIT Press, 2006.
  • KONONENKO, I., KUKAR, M. Machine learning and data mining. Amsterdam: Elsevier, 2007.
  • STEINWART, I., CHRISTMANN, A. Support vector machines. Berlin: Springer, 2008.


Study plans that include the course
Faculty Study plan (Version) Category of Branch/Specialization Recommended year of study Recommended semester
University of Pardubice, date of update: 16.03.2026 23:51. Data created for academic year 2025/2026