Master
2021/2022
Introduction to Deep Learning
Type:
Elective course (Financial Engineering)
Area of studies:
Finance and Credit
Delivered by:
Практико-ориентированные магистерские программы факультета экономических наук
Where:
Faculty of Economic Sciences
When:
2 year, 2 module
Mode of studies:
distance learning
Online hours:
26
Open to:
students of one campus
Instructors:
Sergey V. Kurochkin
Master’s programme:
Financial Engineering
Language:
English
ECTS credits:
3
Contact hours:
2
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
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