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CZECH TECHNICAL UNIVERSITY IN PRAGUE
STUDY PLANS
2019/2020

Data Mining Algorithms

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Code Completion Credits Range Language
MI-ADM.16 Z,ZK 5 2P+1C Czech
Lecturer:
Daniel Vašata, Pavel Kordík (guarantor), Karel Klouda
Tutor:
Daniel Vašata, Pavel Kordík (guarantor), Karel Klouda
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/
Time-table for winter semester 2019/2020:
Time-table is not available yet
Time-table for summer semester 2019/2020:
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
Fri
roomTH:A-s135
Klouda K.
Kordík P.

11:00–12:30
(lecture parallel1)
Thákurova 7 (FSv-budova A)
As135
roomT9:343
Klouda K.
Vašata D.

14:30–16:00
ODD WEEK

(lecture parallel1
parallel nr.103)

Dejvice
NBFIT učebna
roomT9:343
Klouda K.
Vašata D.

16:15–17:45
ODD WEEK

(lecture parallel1
parallel nr.102)

Dejvice
NBFIT učebna
roomT9:348
Klouda K.
Vašata D.

12:45–14:15
EVEN WEEK

(lecture parallel1
parallel nr.101)

Dejvice
NBFIT PC ucebna
Thu
Fri
The course is a part of the following study plans:
Data valid to 2020-05-26
For updated information see http://bilakniha.cvut.cz/en/predmet4663206.html