2026/2027

Machine Learning in Bioinformatics

Type: Mago-Lego

Delivered by: Big Data and Information Retrieval School

Where: Faculty of Computer Science

When: 1, 2 module

Online hours: 14

Open to: students of all HSE University campuses

Instructors: Maria Poptsova

Language: English

ECTS credits: 6

Contact hours: 56

Course Syllabus

Abstract

The course "Introduction to Machine Learning for Bioinformatics" introduces students to the theory and practice of using machine learning algorithms to solve problems in this field. The main goal is to provide students with a comprehensive understanding of modern methods of data analysis and the construction of predictive models. During the course, students will learn the key stages of working with data: from preprocessing and dimensionality reduction methods to techniques for building, optimizing, and validating models. The course program covers a wide range of algorithms, including linear regression with regularization (ridge regression, lasso, elastic network), support vector machine (SVM), neural networks, k-nearest neighbor (k-NN) method, classification and regression trees, as well as ensemble methods such as random forest and gradient boosting. Special attention is paid to practical work: seminars are aimed at developing skills in working with specialized software tools and libraries for predictive modeling. The classes will cover a variety of real-world cases and applied problems based on datasets from the field of bioinformatics.