Data Mining Algorithms
- Daniel Vašata, Pavel Kordík (guarantor), Rodrigo Augusto Da Silva Alves, Karel Klouda
- Daniel Vašata, Pavel Kordík (guarantor), Karel Klouda
- Department of Applied Mathematics
The course focuses on algorithms used in the fields of machine learning and data mining. However, this is not an introductory course, and the students should know machine learning basics. The emphasis is put on advanced algorithms (e.g., gradient boosting) and non-basic kinds of machine learning tasks (e.g., recommendation systems) and models (e.g., kernel methods).
Statistics, algorithmization, BIE-VZD - Introduction to data mining.
- Syllabus of lectures:
1. Recalling basic data mining methods and their applications.
2. Model evaluation.
3. Bias-variance decomposition, negative correlation learning.
4. Decision trees and ensemble methods based on them.
5.-6. (2) Boosting and gradient boosting (XGBoost).
7. Introduction to kernel methods.
8. Kernel methods.
9. Modern kernel methods.
10. - 11. (2) Introduction to recommendation systems, usage of kNN.
12. Matrix factorisation for reccomendation.
13. Hyperparameters tuning, AutoML, new trends.
- Syllabus of tutorials:
(1-6) Various topics and in-depth examples of model evaluation techniques and selected algorithms.
- Study Objective:
The course is suitable for those who want to familiarize themselves with the exceedingly interesting and useful discipline of data mining. The course covers the most useful algorithms that can be easily applied in any field of science.
- Study materials:
1. Hastie, T. - Tibshirani, R. - Friedman, J. : The Elements of Statistical Learning, Data Mining, Inference and Prediction. Springer, 2011. ISBN 978-0387848570.
2. Murphy, K. P. : Machine Learning: A Probabilistic Perspective (Adaptive Computation and Machine Learning series). MIT Press, 2012. ISBN 978-0262018029.
3. Shai Shalev-Shwartz, Shai Ben-David : Understanding Machine Learning, From Theory to Algorithms. Cambridge University Press, 2014. ISBN 978-1107057135.
4. Aggarwal, Ch. C. : Recommender Systems. Springer, 2016. ISBN 978-3319296579.
- Further information:
- Time-table for winter semester 2021/2022:
- Time-table is not available yet
- Time-table for summer semester 2021/2022:
NBFIT PC ucebnaroomT9:303
NBFIT PC ucebnaroomT9:303
NBFIT PC ucebna
Thákurova 7 (FSv-budova A)
- The course is a part of the following study plans:
- Master specialization Computer Science, in Czech, 2018-2019 (PS)
- Master specialization Computer Security, in Czech, 2020 (elective course)
- Master specialization Design and Programming of Embedded Systems, in Czech, 2020 (elective course)
- Master specialization Computer Systems and Networks, in Czech, 202 (elective course)
- Master specialization Management Informatics, in Czech, 2020 (elective course)
- Master specialization Software Engineering, in Czech, 2020 (elective course)
- Master specialization System Programming, in Czech, 2020 (elective course)
- Master specialization Web Engineering, in Czech, 2020 (elective course)
- Master specialization Knowledge Engineering, in Czech, 2020 (PS, elective course)
- Master specialization Computer Science, in Czech, 2020 (PS, elective course)
- Mgr. programme, for the phase of study without specialisation, ver. for 2020 and higher (VO, elective course)