Logo ČVUT
CZECH TECHNICAL UNIVERSITY IN PRAGUE
STUDY PLANS
2018/2019

Data Mining

Login to KOS for course enrollment Display time-table
Code Completion Credits Range Language
BIE-VZD Z,ZK 4 2P+2C
Lecturer:
Kamil Dedecius, Juan Pablo Maldonado Lopez
Tutor:
Kamil Dedecius, Juan Pablo Maldonado Lopez
Supervisor:
Department of Applied Mathematics
Synopsis:

Students are introduced to the basic methods of discovering knowledge in data. In particular, they learn the basic techniques of data preprocessing, multidimensional data visualization, statistical techniques of data transformation, and fundamental principles of knowledge discovery methods. Students will be aware of the relationships between model bias and variance and will know the fundamentals of assessing model quality. Data mining software is extensively used in the module. Students will be able to apply basic data mining tools to common problems (classification, regression, clustering).

Requirements:

-

Syllabus of lectures:

1. Introduction to data mining, data preparation, data visualization.

2. Statistical analysis of data.

3. Cluster analysis.

4. Data model, nearest neighbour classifier.

5. Training, validation and testing, model's quality evaluation.

6. Artificial neural networks in data mining.

7. Unsupervised neural networks - competitive learning.

8. Probability and Bayesian classification.

9. Decision trees and rules.

10. Neural networks with supervised learning.

11. Combining neural networks and models in general.

12. Data mining in the Clementine environment.

13. Text mining, Web mining, selected applications, new trends.

Syllabus of tutorials:

1. Data, visualization, statistics.

2. Statistical analysis of data.

3. Data preprocessing, dimension reduction, relevance of inputs.

4. Model, learning, testing, model validation.

5. Data mining process, classification, prediction, modeling.

6. Cluster analysis, SOM.

7. Project assignment.

8. [3] Consultations, working on projects.

9. [3] Presentations of results, workshop.

Study Objective:

The module aims to introduce students to a rapidly developing field - knowledge discovery in data.

Study materials:

1. Larose, D. T. ''Discovering Knowledge in Data: An Introduction to Data Mining''. Wiley-Interscience, 2004. ISBN 0471666572.

Note:
Further information:
https://courses.fit.cvut.cz/BIE-VZD/
Time-table for winter semester 2018/2019:
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
roomT9:343
Maldonado Lopez J.
09:15–10:45
(lecture parallel1)
Dejvice
NBFIT učebna
Thu
roomT9:348
Maldonado Lopez J.
11:00–12:30
(lecture parallel1
parallel nr.101)

Dejvice
NBFIT PC ucebna
Fri
Time-table for summer semester 2018/2019:
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
roomT9:301
Dedecius K.
14:30–16:00
(lecture parallel1)
Dejvice
NBFIT učebna
roomT9:303
Dedecius K.
16:15–17:45
(lecture parallel1
parallel nr.101)

Dejvice
NBFIT PC ucebna
Tue
Fri
Thu
Fri
The course is a part of the following study plans:
Data valid to 2019-07-23
For updated information see http://bilakniha.cvut.cz/en/predmet1449206.html