SubjectsSubjects(version: 964)
Course, academic year 2024/2025
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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
Is provided by: NPFL104
Additional information: https://ufal.mff.cuni.cz/courses/npfl104
Note: course can be enrolled in outside the study plan
enabled for web enrollment
Guarantor: prof. 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
Annotation -
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.
Last update: T_UFAL (09.05.2012)
Course completion requirements -

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.

Test

  • 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.

Grading

  • 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)
Last update: Žabokrtský Zdeněk, prof. Ing., Ph.D. (13.06.2019)
Literature -
  • 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.

Last update: Vidová Hladká Barbora, doc. Mgr., Ph.D. (25.01.2019)
Syllabus -
  • implementation of basic ML methods for classification and regression
  • learning to use selected ML libraries
  • experimental comparison of performance characteristics of different classification

methods

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

Last update: T_UFAL (09.05.2012)
 
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