Mathematical Foundations of Data Analysis

Course code

MLM6317.DT

old course code

Course title in Estonian

Andmeanalüüsi matemaatilised alused

Course title in English

Mathematical Foundations of Data Analysis

ECTS credits

6.0

Assessment form

assessment

lecturer of 2023/2024 Spring semester

Not opened for teaching. Click the study programme link below to see the nominal division schedule.

lecturer of 2024/2025 Autumn semester

Not opened for teaching. Click the study programme link below to see the nominal division schedule.

Course aims

Introduction to the mathematical foundations of data analysis.

Brief description of the course

Clustering: Voronoi diagrams and Delaunay tesselation, principal component analysis, gradient descent, distances, linear regression.

Learning outcomes in the course

Upon completing the course the student:

- applies principal component analysis;

- uses gradient descent method for finding the best values of the parameters of a given model;

- is able to choose the suitable method for clustering data that matches to the predefined criteria;

- is able to calculate linear regression and to interprete the results.

- applies principal component analysis;

- uses gradient descent method for finding the best values of the parameters of a given model;

- is able to choose the suitable method for clustering data that matches to the predefined criteria;

- is able to calculate linear regression and to interprete the results.

Teacher

Alar Leibak

Prerequisite course 1

Prerequisite course 2

Study programmes containing that course