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 USII/FSUC
Organizational form of instruction Lecture + Lesson
Level of course Master
Year of study not specified
Semester Winter and summer
Number of ECTS credits 5
Language of instruction Czech
Status of course Compulsory
Form of instruction Face-to-face
Work placements This is not an internship
Recommended optional programme components None
Lecturer(s)
  • Chrastina Tomáš, Ing.
  • Hájek Petr, prof. Ing. Ph.D.
Course content
The concept of machine learning and learning theory. Types of machine learning tasks. Generative and discriminative algorithms. Support vector machines and support vector regression. Data partitioning for machine learning. Feature selection methods. Ensembles of algorithms for machine learning. Algorithms for unsupervised learning. Evaluation of learning algorithms. Reinforcement learning. Examples of machine learning applications.

Learning activities and teaching methods
unspecified
  • Contact teaching - 52 hours per semester
  • Home preparation for classes - 28 hours per semester
  • Preparation for an exam - 20 hours per semester
  • Preparation for a credit (assessment) - 10 hours per semester
  • Individual project - 40 hours per semester
Learning outcomes
The aim of the course is to introduce students to advanced approaches in the field of machine learning and how they can be applied to various types of learning tasks in industrial practice.

Prerequisites
unspecified

Assessment methods and criteria
Written examination

Recommended literature
  • AGGARWAL, CH.C. Neural networks and deep learning: a textbook. Cham: Springer, 2018.
  • BISCHOP, C.M. Pattern recognition and machine learning. New York: Springer, 2007.
  • DEISENROTH, M. P., FAISAL, A. A., ONG, Ch. S. Mathematics for machine learning. Cambridge: Cambridge University Press, 2020.
  • GOODFELLOW, I., BENGI, Y., COURVILLE, A. Deep learning. Cambridge: MIT Press, 2016.
  • HASTIE, T., TIBSHIRANI, R., FRIEDMAN, J. The elements of statistical learning. Berlin: Springer, 2009.
  • KELLEHER, J.D., MAC NAMEE, B., D'ARCY, A. Fundamentals of machine learning for predictive data analytics: algorithms, worked examples, and case studies. Cambridge: The MIT Press, 2015.
  • 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