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Introduction to machine learning - NMMB338

Title:	Základy strojového učení
Guaranteed by:	Department of Algebra (32-KA)
Faculty:	Faculty of Mathematics and Physics
Actual:	from 2025
Semester:	summer
E-Credits:	5
Hours per week, examination:	summer s.:2/2, C+Ex [HT]
Capacity:	unlimited
Min. number of students:	unlimited
4EU+:	no
Virtual mobility / capacity:	no
State of the course:	taught
Language:	English
Teaching methods:	full-time
Is provided by:	NMFP436

Guarantor:	RNDr. Karel Kozmík, Ph.D. Mgr. Ondřej Týbl, Ph.D. doc. RNDr. Michal Pešta, Ph.D.
Class:	M Mgr. MMIB > Povinně volitelné
Classification:	Informatics > Software Applications Mathematics > Probability and Statistics
Co-requisite :	NMMB343
Incompatibility :	NMFP436
Interchangeability :	NMFP436
Is incompatible with:	NMFP436

Opinion survey results SS schedule Noticeboard

Annotation -

Introduction to machine learning, motivation, examples of use.

Last update: Žemlička Jan, doc. Mgr. et Mgr., Ph.D. (23.05.2025)

Aim of the course -

An introduction to basic machine learning principles and its use in practice.

Last update: Žemlička Jan, doc. Mgr. et Mgr., Ph.D. (23.05.2025)

Course completion requirements -

Details can be found on the webpage: https://www2.karlin.mff.cuni.cz/~kozmikk/DS2.php

Last update: Žemlička Jan, doc. Mgr. et Mgr., Ph.D. (23.05.2025)

Literature -

Yoshua Bengio, Ian Goodfellow, Aaron Courville: Deep learning, MIT Press, In preparation.

Jürgen Schmidhuber: Deep learning in neural networks: An overview, Neural networks 61 (2015): 85-117.

Friedman, J. H. (March 1999): Stochastic Gradient Boosting, Computational Statistics and Data Analysis, vol. 38, pp. 367-378

Last update: Žemlička Jan, doc. Mgr. et Mgr., Ph.D. (23.05.2025)

Teaching methods -

Lecture + exercises.

Last update: Žemlička Jan, doc. Mgr. et Mgr., Ph.D. (23.05.2025)

Course assessment methods and requirements for successful completion, grading scheme -

Exam will include solving a practical task in Python with discussion about selected algorithm, its theoretial background and results achived in the practical task. Student will receive a data set together with a description of the prediction task which needs to be solved. Course credit is required for examination.

Last update: Žemlička Jan, doc. Mgr. et Mgr., Ph.D. (23.05.2025)

Syllabus -

Lectures:

• introduction to machine learning, motivation, examples

• general methods in machine learning: split of dataset to training and validation, over-fitting, regularization

• methods using decision trees: decision trees, random forest, gradient boosting

• methods using neural networks: simple neural networks, convolutional neural networks, recurrent neural networks

• clustering methods – supervised vs unsupervised

• other classification methods – support vector machine, naive Bayes

Practicals:

• Practicals will be held in computer lab and Python language will be used

• Machine learning algorithms will be applied on real data

Last update: Žemlička Jan, doc. Mgr. et Mgr., Ph.D. (23.05.2025)

Course registration requirements -

Necessary:

Basic calculus: derivatives, integrals, Taylor expansion, etc.

Basic probability and statistics: probability distributions, central limit theorem, statistical tests and hypotheses, Fisher information, maximum likelihood estimators

Basic programming skills (in any language)

Good to know:

Python: some basics will be covered, but can be challenging if the student has no experience with Python

Last update: Žemlička Jan, doc. Mgr. et Mgr., Ph.D. (23.05.2025)