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CZECH TECHNICAL UNIVERSITY IN PRAGUE
STUDY PLANS
2021/2022

Reading group in data mining and machine learning

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Code Completion Credits Range
XP36RGM ZK 4 2P
Lecturer:
Filip Železný, Jiří Kléma (guarantor)
Tutor:
Filip Železný, Jiří Kléma (guarantor)
Supervisor:
Department of Computer Science
Synopsis:

Data mining (DM) aims at revealing non-trivial, hidden and ultimately applicable knowledge in large data. Data size and data heterogeneity make two key data mining technical issues to be solved. The main goal is to understand the patterns that drive the processes generating the data. Machine learning (ML) focuses at computer algorithms that can improve automatically through experience and by the use of data. It often puts emphasis on performance that the algorithms reach. The distinction between DM and ML is not strict as machine learning is often used as a means of conducting useful data mining. For this reason, we cover both the areas in the same course. The main goal of the course is to get acquainted with advanced and modern topics in the field.

Requirements:
Syllabus of lectures:
Syllabus of tutorials:
Study Objective:
Study materials:

1. Rajaraman, A., Leskovec, J., Ullman, J. D.: Mining of Massive Datasets, Cambridge University Press, 2011.

2. Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning: Data Mining, Inference, and Prediction. 2nd ed. New York: Springer, 2009.

3. Peng, R. D., Matsui, E.: The Art of Data Science. A Guide for Anyone Who Works with Data. Skybrude Consulting, 200, 162, 2015.

Note:
Time-table for winter semester 2021/2022:
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
Wed
Thu
Fri
roomKN:E-205
Kléma J.
11:00–12:30
(lecture parallel1)
Karlovo nám.
Koníčkova konzultačka
Time-table for summer semester 2021/2022:
Time-table is not available yet
The course is a part of the following study plans:
Data valid to 2022-08-18
For updated information see http://bilakniha.cvut.cz/en/predmet6633906.html