SubjectsSubjects(version: 952)
Course, academic year 2019/2020
  
Data Mining Algorithms - N500012
Title: Algoritmy data miningu
Guaranteed by: CTU in Prague, Faculty of Information Technology (500)
Faculty: University of Chemistry and Technology, Prague
Actual: from 2017 to 2020
Semester: summer
Points: summer s.:4
E-Credits: summer s.:4
Examination process: summer s.:
Hours per week, examination: summer s.:2/1, C+Ex [HT]
Capacity: unknown / unknown (unknown)
Min. number of students: unlimited
Language: Czech
Teaching methods: full-time
Teaching methods: full-time
Level:  
For type:  
Guarantor: Holeňa Martin doc. Ing. RNDr.
Is interchangeable with: M500002
Examination dates   Schedule   
Annotation -
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.
Last update: Jirát Jiří (31.01.2014)
Aim of the course -

Students will be able to:

use theoretical background that is needed for skillful application of data mining algoritms in the field of classification, regression and clustering

Last update: Jirát Jiří (31.01.2014)
Literature -

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

Last update: Jirát Jiří (31.01.2014)
Learning resources -

https://edux.fit.cvut.cz/courses/MI-ADM

(login necessary)

Last update: Jirát Jiří (31.01.2014)
Syllabus -

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

Last update: Jirát Jiří (31.01.2014)
Registration requirements -

Statistics

Last update: Jirát Jiří (31.01.2014)
Teaching methods
Activity Credits Hours
Účast na přednáškách 1 28
Práce na individuálním projektu 2.2 61
Účast na seminářích 0.5 14
4 / 4 103 / 112
 
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