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CZECH TECHNICAL UNIVERSITY IN PRAGUE
STUDY PLANS
2018/2019

Data Mining Algorithms

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Code Completion Credits Range Language
MI-ADM.16 Z,ZK 5 2+1 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 is suitable for those who want to familiarize themselves with the exceedingly interesting and useful discipline of data mining. The course covers the most useful algorithms that can be easily applied in any field of science.

Requirements:

Statistics

Syllabus of lectures:

1) Introduction to data mining, classification, prediction, K-NN algorithm and variants

2) Model, evaluation, plasticity regularization

3) Classification and Regression from statistical point of view

4) Decision Trees (C4.5, CART, MARS algorithms)

5) Classification by means of perceptrons and its generalization

6) Linear, polynomial and logistic regression, LMS, MLE algorithms

7) Nonlinear SVM-classifiers and the SV-regression

8) Inductive modelling - GMDH MIA, COMBI

9) Nonlinear regression by multilayered perceptrons

10) Ensemble models (Adaboost algorithm)

11) Statistical approach to neural networks

12) Cluster analysis (K-means, agglomerative clustering, neural gas, SOM)

13) A statistical approach to number of hidden neurons selection

Syllabus of tutorials:

Semestral project

Study Objective:

The course is suitable for those who want to familiarize themselves with the exceedingly interesting and useful discipline of data mining. The course covers the most useful algorithms that can be easily applied in any field of science.

Study materials:

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

Note:
Time-table for winter semester 2018/2019:
Time-table is not available yet
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
Tue
Fri
roomTH:A-s135
Klouda K.
Vašata D.

14:30–16:00
(lecture parallel1)
Thákurova 7 (FSv-budova A)
As135
Thu
roomT9:348
Klouda K.
Vašata D.

12:45–14:15
EVEN WEEK

(lecture parallel1
parallel nr.101)

Dejvice
NBFIT PC ucebna
roomT9:351
Klouda K.
Vašata D.

14:30–16:00
ODD WEEK

(lecture parallel1
parallel nr.102)

Dejvice
NBFIT PC ucebna
roomT9:351
Klouda K.
Vašata D.

16:15–17:45
ODD WEEK

(lecture parallel1
parallel nr.103)

Dejvice
NBFIT PC ucebna
Fri
The course is a part of the following study plans:
Data valid to 2019-02-21
For updated information see http://bilakniha.cvut.cz/en/predmet4663206.html