Master
2024/2025
Computation and optimization for machine learning
Type:
Elective course (Mathematics)
Area of studies:
Mathematics
Delivered by:
Department of Fundamental Mathematics
When:
2 year, 1, 2 module
Mode of studies:
distance learning
Online hours:
20
Open to:
students of one campus
Instructors:
Elena Nozdrinova
Master’s programme:
Mathematics
Language:
English
ECTS credits:
6
Contact hours:
6
Course Syllabus
Abstract
Course starts with a basic introduction to concepts concerning functional mappings. Later students are assumed to study limits (in case of sequences, single- and multivariate functions), differentiability (once again starting from single variable up to multiple cases), integration, thus sequentially building up a base for the basic optimisation. To provide an understanding of the practical skills set being taught, the course introduces the final programming project considering the usage of optimisation routine in machine learning.
Additional materials provided during the course include interactive plots in GeoGebra environment used during lectures, bonus reading materials with more general methods and more complicated basis for discussed themes