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CZECH TECHNICAL UNIVERSITY IN PRAGUE
STUDY PLANS
2019/2020

Selected Parts of Data Mining

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Code Completion Credits Range
XP36VPD ZK 4 2P+2S
Lecturer:
Jiří Kléma (guarantor)
Tutor:
Jiří Kléma (guarantor)
Supervisor:
Department of Computer Science
Synopsis:

Data mining aims at revealing non-trivial, hidden and ultimately applicable knowledge in large data. This course focuses on two key data mining issues: data size and their heterogeneity. When dealing with large data, it is important to resolve both the technical issues such as distributed computing or hashing and general algorithmic complexity. In this part, the course will be motivated mainly by case studies on web and social network mining. The second part will discuss approaches that merge heterogeneous prior knowledge with measured data. Bioinformatics will make the main application field here. It is assumed that students have completed the master course on Machine Learning and Data Analysis (A4M33SAD).

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

Anand Rajaraman, Jure Leskovec, Jeffrey D. Ullman: Mining of Massive Datasets, Cambridge University Press, 2011.

Note:
Time-table for winter 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
Thu
roomKN:E-307
Kléma J.
11:00–12:30
(lecture parallel1)
Karlovo nám.
HW-lab K307
roomKN:E-307
Kléma J.
12:45–14:15
(lecture parallel1
parallel nr.101)

Karlovo nám.
HW-lab K307
Fri
Time-table for summer semester 2019/2020:
Time-table is not available yet
The course is a part of the following study plans:
Data valid to 2019-10-18
For updated information see http://bilakniha.cvut.cz/en/predmet2968906.html