SubjectsSubjects(version: 945)
Course, academic year 2023/2024
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Machine Learning Methods - NPFX104
Title: Metody strojového učení
Guaranteed by: Student Affairs Department (32-STUD)
Faculty: Faculty of Mathematics and Physics
Actual: from 2022
Semester: summer
E-Credits: 5
Hours per week, examination: summer s.:1/2, C+Ex [HT]
Capacity: unlimited
Min. number of students: unlimited
4EU+: no
Virtual mobility / capacity: no
State of the course: taught
Language: Czech
Teaching methods: full-time
Teaching methods: full-time
Is provided by: NPFL104
Additional information:
Note: course can be enrolled in outside the study plan
enabled for web enrollment
Guarantor: doc. Ing. Zdeněk Žabokrtský, Ph.D.
doc. RNDr. Ondřej Bojar, Ph.D.
Class: DS, matematická lingvistika
Informatika Mgr. - Matematická lingvistika
Pre-requisite : {NXXX011, NXXX012, NXXX013, NXXX038, NXXX039, NXXX040, NXXX067, NXXX069, NXXX070, NXXX071}
Incompatibility : NPFL104
Interchangeability : NPFL104
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Annotation -
Last update: T_UFAL (09.05.2012)
The course is focused on practical exercises with applying machine learning techniques to real data. Students are expected to be familiar with basic machine learning concepts.
Course completion requirements -
Last update: doc. Ing. Zdeněk Žabokrtský, Ph.D. (13.06.2019)

To pass the course, you will need to submit homework assignments and do a written test.

Homework assignments

  • Assignments will be set in the class and specified on the website.
  • To get the credit, you need to get at least 50% of the total achievable points for the assignments.
  • If you miss the deadline, there is a second deadline in 2 weeks, but your points for the assignment will be multiplied by 0.5; after the second deadline, you get 0 points.


  • There will be a written test at the end of the semester.
  • To pass the exam, you need to get at least 50% of the total points from the test.


  • Your grade is based on the average of your performance; the test and the homework assignments are weighted 1:1.
  • ≥ 90%: grade 1 (excellent)
  • ≥ 70%: grade 2 (very good)
  • ≥ 50%: grade 3 (good)
  • < 50%: grade 4 (fail)
Literature -
Last update: doc. Mgr. Barbora Vidová Hladká, Ph.D. (25.01.2019)
  • Christopher M. Bishop: Pattern Recognition and Machine Learning. Springer Verlag, 2006
  • Trevor Hastie, Robert Tibshirani, Jerome Friedman: The Elements of Statistical

Learning. Springer Verlag, 2001.

Syllabus -
Last update: T_UFAL (09.05.2012)
  • implementation of basic ML methods for classification and regression
  • learning to use selected ML libraries
  • experimental comparison of performance characteristics of different classification


  • feature engineering
  • ensemble techniques
  • implementation of basic techniques of unsupervised ML

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