Computation and optimization for machine learning

2024/2025

Type: Mago-Lego

Delivered by: Department of Fundamental Mathematics

When: 1, 2 module

Online hours: 20

Open to: students of one campus

Instructors: Elena Nozdrinova

Language: English

ECTS credits: 6

Full Syllabus Ask Question

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

Learning Objectives

Here we introduce basic concept the calculus course could not be imagine without: function. In order to properly do it, one should say that the function is a mapping from one set to another. Thus, we start with the ideas of numerical sets and mapping, then proceeding with functions itself. Since we are particularly interested in functions' graph, we spend a lot of time discussing simplest ways to produce a complex function graph from elementary case. In the second part of the week we start our calculus journey with a discrete limit, the limit of sequences, and master skills needed to calculate them.