Practical Machine Learning Methods for Data Mining

2025/2026

Academic Year

ENG

Instruction in English

6

ECTS credits

Delivered at:

Department of Economics

Course type:

Compulsory course

When:

1 year, 3, 4 module

Instructor

Сысоев Дмитрий Сергеевич

Course Syllabus

Abstract

Using data to make predictions, test hypotheses and estimate models is an important skill in the current job market. Many companies collect a lot of data and their decisions data-driven. Machine learning disrupts many fields and promises to achieve superhuman performance in the coming decades. Statistical analysis allows to test hypotheses and verify which of the models fits the data best. In this course we will cover different methods for supervised and unsupervised learning to develop a necessary toolkit for successful data scientists. For some of the methods we will go into details to learn why and how they work. Also we will touch on ethical implications of data science in the age of big data and apply learned methods to real business data sets.