Programming and Modelling 2

Course guarantor:

Lecturer:

Tutor:

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:

Further information:

No time-table has been prepared for this course

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)