Introduction to Deep Learning

2021/2022

Учебный год

ENG

Обучение ведется на английском языке

3

Кредиты

Кто читает:

Практико-ориентированные магистерские программы факультета экономических наук

Статус:

Курс по выбору

Когда читается:

2-й курс, 2 модуль

Преподаватель

Курочкин Сергей Владимирович

Course Syllabus

Abstract

This specialization gives an introduction to deep learning, reinforcement learning, natural language understanding, computer vision and Bayesian methods. Top Kaggle machine learning practitioners and CERN scientists will share their experience of solving real-world problems and help you to fill the gaps between theory and practice. Upon completion of 7 courses you will be able to apply modern machine learning methods in enterprise and understand the caveats of real-world data and settings.

Learning Objectives

• The goal of this course is to give learners basic understanding of modern neural networks and their applications in computer vision and natural language understanding.

Expected Learning Outcomes

• We'll consider the reinforcement learning formalisms in a more rigorous, mathematical way. You'll learn how to effectively compute the return your agent gets for a particular action - and how to pick best actions based on that return.
• We'll find out how to apply last week's ideas to the real world problems: ones where you don't have a perfect model of your environment.
• We'll learn to scale things even farther up by training agents based on neural networks.
• You'll learn how to build better exploration strategies with a focus on contextual bandit setup

Course Contents

• Intro: why should i care?
• At the heart of RL: Dynamic Programming
• Model-free methods
• Approximate Value Based Methods
• Policy-based methods
• Exploration

Assessment Elements

• Tests
• Tasks

Interim Assessment

• 2021/2022 2nd module
  0.5 * Tasks + 0.5 * Tests

Bibliography

Recommended Core Bibliography

• Fabozzi, F. J. (2002). The Handbook of Financial Instruments. Hoboken, N.J.: Wiley. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=81949
• Анализ данных на компьютере, Тюрин, Ю. Н., 2003

Recommended Additional Bibliography

• Microsoft SQL Server 2005 Analysis Services. OLAP и многомерный анализ данных, Бергер, А., 2007