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The subject matter of a course is described in various texts.

NIE-ADM Data Mining Algorithms Extent of teaching: 2P+1C

Instructor: Da Silva Alves R., Kordík P. Completion: Z,ZK

Department: 18105 Credits: 5 Semester: L

Annotation:
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).

Lecture syllabus:

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.

Seminar syllabus:
(1-6) Various topics and in-depth examples of model evaluation techniques and selected algorithms

Literature:

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.

Requirements:
Statistics, algorithmization, BIE-VZD - Introduction to data mining.

Chybí osnova cvičení a odkaz na webovou stránku předmětu. Informace o předmětu a výukové materiály naleznete na https://courses.fit.cvut.cz/MI-ADM/

The course is also part of the following Study plans:

Study Plan Study Branch/Specialization Role Recommended semester

NIE-NPVS.21 Design and Programming of Embedded Systems 2021 V 2

NIE-PSS.21 Computer Systems and Networks 2021 V 2

NIE-SI.21 Software Engineering 2021 V 2

NIE-PB.21 Computer Security 2021 V 2

NIE-TI.21 Computer Science 2021 PS 2

Page updated 26. 4. 2024, semester: Z/2020-1, L/2021-2, L/2019-20, L/2022-3, Z/2019-20, L/2020-1, L/2023-4, Z/2022-3, Z/2021-2, Z/2023-4, Z/2024-5, Send comments to the content presented here to Administrator of study plans Design and implementation: J. Novák, I. Halaška

NIE-ADM	Data Mining Algorithms			Extent of teaching:	2P+1C
Instructor:	Da Silva Alves R., Kordík P.			Completion:	Z,ZK
Department:	18105	Credits:	5	Semester:	L

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.

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.