Pattern Recognition and Machine Learning

Computer Science and ICT, Data, AI

About this course

The basic formulations of the statistical decision problem are presented. The necessary knowledge about the (statistical) relationship between observations and classes of objects is acquired by learning on the raining set. The course covers both well-established and advanced classifier learning methods, as Perceptron, AdaBoost, Support Vector Machines, and Neural Nets.

This course is also part of the inter-university programme Minor. It pools the best of AI education in Prague to provide students with a deeper and broader insight into the field of artificial intelligence. More information is available at

Learning outcomes

To teach the student to formalize statistical decision making problems, to use machine learning techniques and to solve pattern recognition problems with the most popular classifiers (SVM, AdaBoost, neural net, nearest neighbour).

Course requirements

Knowledge of linear algebra, mathematical analysis and probability and statistics.


Lectures, Practises, Self-study, Exercises, Tutorial sessions

Additional information

  • Credits
    ECTS 6
  • Contact hours per week
  • Instructors
    Ing. Vojíř Tomáš Ph.D., Mgr. Šochman Jan Ph.D., Mgr. Drbohlav Ondřej Ph.D., prof. Ing. Matas Jiří Ph.D., Mgr. Shekhovtsov Oleksandr Ph.D., Ing. Neumann Lukáš Ph.D.
  • Mode of instruction
If anything remains unclear, please check the FAQ of CTU (Czech Republic).


  • Start date

    23 September 2024

    • Ends
      16 February 2025
    • Term *
      Winter 2024/2025
    • Instruction language
    • Register between
      15 May - 29 Jul 2024
    Only 8 days to enrol
    Apply now
These offerings are valid for students of L'X (France)