Master 2024/2025

Research Seminar "Deep Learning"

Type: Compulsory course (Master of Data Science)

Area of studies: Applied Mathematics and Informatics

Delivered by: Big Data and Information Retrieval School

Where: Faculty of Computer Science

When: 2 year, 2 module

Mode of studies: offline

Open to: students of one campus

Instructors: Yury Sanochkin

Master’s programme: Master of Data Science

Language: English

ECTS credits: 3

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.