Fundamentals of Machine Learning
| Type of study | Follow-up Master |
|---|---|
| Language of instruction | Czech |
| Code | 460-8703/01 |
| 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. 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.
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.
E-learning
All materials are published on the e-learning portal (https://www.vsb.cz/e-vyuka/en)
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.
Advised literature
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 .