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