Programming and Modelling 2
Code | Completion | Credits | Range | Language |
---|---|---|---|---|
21PAM2 | KZ | 5 | 2P+4C+16B | Czech |
- Course guarantor:
- Lecturer:
- Lenka Hanáková, Vladimír Socha
- Tutor:
- Lenka Hanáková, Vladimír Socha
- Supervisor:
- Department of Air Transport
- Synopsis:
-
Descriptive statistics, classical statistical analysis. Statistical hypothesis testing. Analysis of variance (ANOVA), one-factor, two-factor ANOVA. Non-parametric methods. Linear regression. Correlation, correlation coefficient. Non-linear regression models, procedure for regression analysis of a non-linear model. Basics of machine learning. Classification by nearest neighbour method. SVM classifiers. Decision trees.
- Requirements:
-
21PAM1
- Syllabus of lectures:
- Syllabus of tutorials:
- Study Objective:
-
The goal of the course is to introduce students to basic data analysis tools. The course is designed so that students are able to apply the acquired information to real data. The output of the course will be the students' own semester project.
- Study materials:
-
Peters, T. (2019). Data-driven science and engineering: machine learning, dynamical systems, and control, Cambridge: Cambridge University Press, 472 s., ISBN 978-1-10-842209-3.
- Note:
- Time-table for winter semester 2024/2025:
-
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 Wed Thu Fri - Time-table for summer semester 2024/2025:
- Time-table is not available yet
- The course is a part of the following study plans:
-
- Master Full-Time PL from 2022/23 (compulsory course)
- Master Part-Time PL from 2022/23 (compulsory course)
- Master Full-Time PL from 2023/24 (compulsory course)
- Master Part-Time PL from 2023/24 (compulsory course)