Research Seminar "Deep Learning"

2024/2025

Academic Year

ENG

Instruction in English

3

ECTS credits

Delivered at:

Big Data and Information Retrieval School

Course type:

Compulsory course

When:

2 year, 2 module

Instructor

Sanochkin, Yury

Course Syllabus

Abstract

This course introduces the students to the elements of machine learning, including supervised and unsupervised methods such as linear and logistic regressions, decision trees, support vector machines, bootstrapping, random forests, boosting, regularized methods. Students will apply Python programming language and popular packages, such as pandas, scikit-learn, to investigate and visualize datasets and develop machine learning models that solve theoretical and data-driven problems. Pre-requisites: at least one semester of calculus on a real line, vector calculus, linear algebra, probability and statistics, computer programming in high level language such as Python.