Fundamentals of Machine Learning
| Type of study | Bachelor |
|---|---|
| Language of instruction | Czech |
| Code | 460-2064/01 |
| Abbreviation | ZSU |
| Course title | Fundamentals of Machine Learning |
| Credits | 4 |
| Coordinating department | Department of Computer Science |
| Course coordinator | prof. Ing. Jan Platoš, Ph.D. |
Subject syllabus
Lectures:
1. Data and their Properties
2. Statistical Data Features
3. Knowledge Representation
4. Basic Algorithms
5. Credibility and Algorithm evaluation
6. Advanced Methods and Algorithms
7. Extending of Linear Model
8. Data Transformation
9. Optimization methods
10. Data Vizualization
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 - linear models.
5. Demonstration of lecture knowledge - model quality and its measurement.
6. Demonstration of lecture knowledge - non=linear models.
7. Demonstration of lecture knowledge - data transformation.
8. Demonstration of lecture knowledge - data transformation.
9. Demonstration of lecture knowledge - optimization method introduction.
10. Demonstration of lecture knowledge - data visualization.
1. Data and their Properties
2. Statistical Data Features
3. Knowledge Representation
4. Basic Algorithms
5. Credibility and Algorithm evaluation
6. Advanced Methods and Algorithms
7. Extending of Linear Model
8. Data Transformation
9. Optimization methods
10. Data Vizualization
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 - linear models.
5. Demonstration of lecture knowledge - model quality and its measurement.
6. Demonstration of lecture knowledge - non=linear models.
7. Demonstration of lecture knowledge - data transformation.
8. Demonstration of lecture knowledge - data transformation.
9. Demonstration of lecture knowledge - optimization method introduction.
10. Demonstration of lecture knowledge - data visualization.
E-learning
All materials are published on the e-learning portal (https://www.vsb.cz/e-vyuka/cs).
Literature
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.
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 .
AGGARWAL, Charu C. Data mining: the textbook. New York, NY: Springer Science+Business Media, 2015. ISBN 978-3-319-14141-1 .