Data Analysis and Machine Learning

July 10 – 19

In-person

Language: English

This course will guide you through different steps of building data-driven solutions as well as introduce you to different methods and tools necessary for solving project tasks.

Course Description

The training is project-based and guides students through different steps of building data-driven solutions. The lecture part introduces the methods and tools necessary for solving project tasks, followed by hands-on practical exercises. The programme also includes lectures with overview of state-of-the-art tasks in Artificial Intelligence and approaches to solving them to shape students' up-to-date understanding of AI methods. At least basic Python programming experience is required.

Why Choose This Course?

Data Analysis and Machine Learning is the hottest topic; ML tools allow solving problems from various subject areas.

Hands-on problems from the industry and state-of-the-art methods of their solution.

Project-based training, practicing with different roles in data-driven project, project to the portfolio.

Intensive practical component and sufficient lecture part.

Content 

Topic 1. Introduction to Data Analysis

Topic 2. Data visualisation

Topic 3. Principles of Machine Learning

Topic 4. Introduction to Neural Networks

Topic 5. Interpretable Machine Learning

Topic 6. Practical application

Teaching Methods

Lectures and practice in programming, group projects, guest overview lectures.

Prerequisites

Computer Science students

at least basic Python programming experience

at least basic knowledge of probability theory and linear algebra

English B2+

Final Assessment 

Group work with the presentation of the results.

Final Grade Background

Contribution to the project, participation in discussions, completion of ongoing exercises.

Course is taught by

Associate Professor Alena Suvorova, Associate Professor Alexander Sirotkin.