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CZECH TECHNICAL UNIVERSITY IN PRAGUE
STUDY PLANS
2018/2019

Data Mining Algorithms

The course is not on the list Without time-table
Code Completion Credits Range Language
MI-ADM Z,ZK 4 2+1 Czech
Lecturer:
Tutor:
Supervisor:
Department of Applied Mathematics
Synopsis:

In this course, we discuss most popular data mining algoritms and optimization techniques such as decision trees, support vector machines, multilayered perceptrons etc. We also explain theoreticaly basic elements of statistical learning that are essential for all data engineers.

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 aim of the course is to teach students a theoretical background that is needed for skillful application of data mining algoritms in the field of classification, regression and clustering.

Study materials:

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

Note:
Further information:
No time-table has been prepared for this course
The course is a part of the following study plans:
Data valid to 2019-02-19
For updated information see http://bilakniha.cvut.cz/en/predmet1697506.html