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Ukončeno v akademickém roce 2020/2021

Fundamentals of Machine Learning

Type of study Follow-up Master
Language of instruction English
Code 460-8703/02
Abbreviation ZSU
Course title Fundamentals of Machine Learning
Credits 4
Coordinating department Department of Computer Science
Course coordinator prof. Ing. Jan Platoš, Ph.D.

Osnova předmětu

Lectures:
1. Data and their Properties
2. Statistical Data Features
3. Knowledge Representation
4. Exploratory analysis I
5. Exploratory analysis II
6. Basic Algorithms - Clustering
7. Basic Algorithms - Classification/Regression
8. Credibility and Algorithm evaluation
9. Advanced Methods and Algorithms
10. Extending of Linear Model
11. Data Transformation
12. Optimization methods
13. Data Visualization I
14. Data Visualization II

Exercises on computer lab:
1. Demonstration of lecture knowledge - data and the properties.
2. Demonstration of lecture knowledge - statistical data proeprties.
3. Demonstration of lecture knowledge - knowledge representations.
4. Demonstration of lecture knowledge - exploratory analysis I
5. Demonstration of lecture knowledge - exploratory analysis II
6. Demonstration of lecture knowledge - custering
7. Demonstration of lecture knowledge - classification
8. Demonstration of lecture knowledge - model quality and its measurement.
9. Demonstration of lecture knowledge - tree based algorithms
10. Demonstration of lecture knowledge - non=linear models.
11. Demonstration of lecture knowledge - data transformation.
12. Demonstration of lecture knowledge - introduction into optimization methods
13. Demonstration of lecture knowledge - data visualization.
14. Demonstration of lecture knowledge - data visualization.

Povinná literatura

Presentation for lectures.
HASTIE, Trevor., Robert. TIBSHIRANI and J. H. FRIEDMAN. The elements of statistical learning: data mining, inference, and prediction. 2nd ed. New York, NY: Springer, c2009. ISBN 978-0-387-84858-7.
WITTEN, Ian H., Eibe FRANK, Mark A. HALL and Christopher J. PAL. Data mining: Practical machine learning tools and techniques. Fourth Edition. Amsterdam: Elsevier, 2017. ISBN 978-0-12-804291-5 .

Doporučená literatura

LESKOVEC, Jurij, Anand RAJARAMAN and Jeffrey D. ULLMAN. Mining of massive datasets / Jure Leskovec, Standford University, Anand Rajaraman, Milliways Labs, Jeffrey David Ullman, Standford University. Second edition. Cambridge: Cambridge University Press, 2014. ISBN 9781107077232 .
AGGARWAL, Charu C. Data mining: the textbook. New York, NY: Springer Science+Business Media, 2015. ISBN 978-3-319-14141-1 .


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