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CZECH TECHNICAL UNIVERSITY IN PRAGUE
STUDY PLANS
2023/2024
UPOZORNĚNÍ: Jsou dostupné studijní plány pro následující akademický rok.

Data Mining Algorithms

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Code Completion Credits Range Language
NI-ADM Z,ZK 5 2P+1C Czech
Garant předmětu:
Pavel Kordík
Lecturer:
Rodrigo Augusto Da Silva Alves, Pavel Kordík, Daniel Vašata
Tutor:
Rodrigo Augusto Da Silva Alves, Pavel Kordík, Daniel Vašata
Supervisor:
Department of Applied Mathematics
Synopsis:

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).

Requirements:

Statistics, algorithmization, BI-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.

Note:
Further information:
https://courses.fit.cvut.cz/NI-ADM/
Time-table for winter semester 2023/2024:
Time-table is not available yet
Time-table for summer semester 2023/2024:
06:00–08:0008:00–10:0010:00–12:0012:00–14:0014:00–16:0016:00–18:0018:00–20:0020:00–22:0022:00–24:00
Mon
Tue
roomTH:A-s135
Da Silva Alves R.
Vašata D.

14:30–16:00
(lecture parallel1)
Thákurova 7 (budova FSv)
As135
roomT9:302
Da Silva Alves R.
Vašata D.

16:15–17:45
EVEN WEEK

(lecture parallel1
parallel nr.101)

Dejvice
NBFIT učebna
roomT9:302
Da Silva Alves R.
Vašata D.

16:15–17:45
ODD WEEK

(lecture parallel1
parallel nr.102)

Dejvice
NBFIT učebna
Wed
roomT9:302
Da Silva Alves R.
Vašata D.

14:30–16:00
ODD WEEK

(lecture parallel1
parallel nr.103)

Dejvice
NBFIT učebna
Thu
Fri
The course is a part of the following study plans:
Data valid to 2024-04-15
Aktualizace výše uvedených informací naleznete na adrese https://bilakniha.cvut.cz/en/predmet6099706.html