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CZECH TECHNICAL UNIVERSITY IN PRAGUE
STUDY PLANS
2021/2022

Data Mining Algorithms

The course is not on the list Without time-table
Code Completion Credits Range Language
MI-ADM.16 Z,ZK 5 2P+1C Czech
Lecturer:
Tutor:
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, 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:
Study Objective:
Study materials:

[1] Hastie T.,Tibshirani R.,Friedman J., The Elements of Statistical Learning, Data Mining, Inference and Prediction, Springer, 2011

[2] Murphy, K. P., Machine Learning: A Probabilistic Perspective (Adaptive Computation and Machine Learning series), The MIT Press, 2012

[3] Aggarwal, Ch. C., Recommender Systems, Springer, 2016

Note:
Further information:
https://courses.fit.cvut.cz/MI-ADM/
No time-table has been prepared for this course
The course is a part of the following study plans:
Data valid to 2021-12-05
For updated information see http://bilakniha.cvut.cz/en/predmet4663206.html