Statistical Data Analysis

The course cannot be taken simultaneously with:

Statistical data analysis (BE4M36SAN)

Garant předmětu:

Jiří Kléma

Lecturer:

Jiří Kléma

Tutor:

Jáchym Barvínek, Jan Blaha, Jiří Kléma, Anh Vu Le, Zdeněk Míkovec, Tomáš Pevný

Supervisor:

Department of Computer Science

Synopsis:

This course builds on the skills developed in introductory statistics courses. It is practically oriented and gives an introduction to applied statistics. It mainly aims at multivariate statistical analysis and modelling, i.e., the methods that help to understand, interpret, visualize and model potentially high-dimensional data. It can be seen as a purely statistical counterpart to machine learning and data mining courses.

Requirements:

The general statistical concepts covered in the course B0B01PST. The knowledge of linear classification, clustering and dimensionality reduction, see B4B33RPZ for details.

Syllabus of lectures:

1. Introduction, motivation, a course map, review of the basic statistical terms and methods.

2. Dimension reduction (PCA and kernel PCA).

3. Dimension reduction (other non-linear methods).

4. Clustering (basic methods, spectral clustering).

5. Clustering (biclustering, semi-supervised clustering)

6. Multivariate confirmation analysis (ANOVA and MANOVA).

7. Discriminant analysis (categorical dependent variable, LDA, logistic regression).

8. Multivariate regression (continuous dependent variable, linear regression, p-values, overfitting)

9. Multivariate regression (non-linear models, polynomial and local regression).

10. Anomaly detection.

11. Robust statistics.

12. Empirical studies, their design and evaluation.

13. Power analysis.

14. The final review, spare lecture.

Syllabus of tutorials:

1. Programming in R, introduction.

2. R libraries, statistical packages, learning package Swirl.

3. Data visualization in R.

4. Dimension reduction - assignment.

5. Clustering - assignment.

6. Multivariate confirmation analysis - assignment.

7. Discriminant analysis - assignment.

8. Mid-term test.

9. Multivariate linear regression - assignment.

10. Multivariate non-linear regression - assignment.

11. Anomaly detection - assignment.

12. Empirical study design - assignment.

13. Power analysis - assignment.

14. Spare lab, credits.

Study Objective:

Study materials:

1. Hair, J. F., et al.: Multivariate Data Analysis: A Global Perspective. 7th ed., Prentice Hall, 2009.

2. James, G. et al.: An Introduction to Statistical Learning with Applications in R., Springer, 2013.

Note:

Further information:

https://cw.fel.cvut.cz/wiki/courses/B4M36SAN

Time-table for winter semester 2022/2023:

	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	roomKN:E-301 Kléma J. 12:45–14:15 (lecture parallel1) Karlovo nám. Šrámkova posluchárna K9roomKN:E-311 Le A. Míkovec Z. 14:30–16:00 (lecture parallel1 parallel nr.101) Karlovo nám. Lab K311roomKN:E-311 Le A. Pevný T. 16:15–17:45 (lecture parallel1 parallel nr.102) Karlovo nám. Lab K311roomKN:E-311 Le A. 18:00–19:30 (lecture parallel1 parallel nr.103) Karlovo nám. Lab K311
Tue
Wed
Thu	roomKN:E-311 Blaha J. 14:30–16:00 (lecture parallel1 parallel nr.104) Karlovo nám. Lab K311
Fri

Time-table for summer semester 2022/2023:

Time-table is not available yet

The course is a part of the following study plans:

• Medical electronics and bioinformatics (compulsory course in the program)
• Open Informatics - Human-Computer Interaction (compulsory course of the specialization)
• Open Informatics - Cyber Security (compulsory course of the specialization)
• Open Informatics - Bioinformatics (compulsory course of the specialization)
• Open Informatics - Data Science (compulsory course of the specialization)
• Medical electronics and bioinformatics (compulsory course in the program)
• Medical electronics and bioinformatics (compulsory course in the program)
• Medical electronics and bioinformatics (compulsory course in the program)