Applied mathematics for machine learning

The course is designed for students without a mathematical background. It will cover the basic concepts necessary to understand the methods underlying machine learning and neural network models. Another goal of the course is to explain how data can be transformed and represented for subsequent model training.

The course consists of 13 lectures and 13 seminars. The lectures present the theoretical foundations of the topics, supported by simple examples. The seminars will focus on analysis, demonstration, and practical application of software implementations and algorithms related to the theoretical concepts covered.

Upon completing the course, students are primarily expected to understand what data representation looks like numerically and how machine learning models process data numerically. At the same time, the course does not aim to teach a rigorous derivation of every formula and theorem; instead, it focuses on the level of understanding required for practical application.

The course program includes 26 sessions: 13 lectures and 13 seminars

Requirements for students:

• Basic knowledge of Python

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Занятия проводятся на Факультете Биоинженерии и биоинформатики

В программе курса 26 занятий: 13 лекций и 13 семинаров

Старт курса: с  28 сентября

Все занятия (кроме вводного) будут проходить по понедельникам:

• лекция 15:35 — 17:10
• семинар 17:20 — 18:55

Формат занятий: онлайн (дистанционно)

Требования к студентам:

• Базовое знание Python

Форма записи на курс

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Набор на курс 2022 года закрыт