This course is part of the Machine Learning and Reinforcement Learning in Finance Specialization

3.6
stars
123 ratings

Igor Halperin

18,342 already enrolled

Offered By

Machine Learning and Reinforcement Learning in Finance SpecializationNew York University

About this Course

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This course aims at introducing the fundamental concepts of Reinforcement Learning (RL), and develop use cases for applications of RL for option valuation, trading, and asset management. 
By the end of this course, students will be able to
- Use reinforcement learning to solve classical problems of Finance such as portfolio optimization, optimal trading, and option pricing and risk management.
- Practice on valuable examples such as famous Q-learning using financial problems.
- Apply their knowledge acquired in the course to a simple model for market dynamics that is obtained using reinforcement learning as the course project.

Prerequisites are the courses "Guided Tour of Machine Learning in Finance" and "Fundamentals of Machine Learning in Finance". Students are expected to know the lognormal process and how it can be simulated. Knowledge of option pricing is not assumed but desirable.

Flexible deadlines

Reset deadlines in accordance to your schedule.

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Coursera Labs

Includes hands on learning projects.

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Course 3 of 4 in the

Machine Learning and Reinforcement Learning in Finance Specialization

Advanced Level

Approx. 17 hours to complete

English

Skills you will gain

option pricing and risk management
simple model for market dynamics
Q-learning using financial problems
optimal trading
Portfolio Optimization

Flexible deadlines

Reset deadlines in accordance to your schedule.

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Coursera Labs

Includes hands on learning projects.

Learn more about Coursera Labs

Course 3 of 4 in the

Machine Learning and Reinforcement Learning in Finance Specialization

Advanced Level

Approx. 17 hours to complete

English

Instructor

Instructor rating

4/5 (8 Ratings)

Igor Halperin

45,806 Learners

4 Courses

Offered by

New York University

Syllabus - What you will learn from this course

Week1

Week 1

4 hours to complete

MDP and Reinforcement Learning

4 hours to complete

14 videos (Total 107 min), 2 readings, 1 quiz

14 videos

Introduction to the Specialization4mPrerequisites7mWelcome to the Course5mIntroduction to Markov Decision Processes and Reinforcement Learning in Finance9mMDP and RL: Decision Policies9mMDP & RL: Value Function and Bellman Equation7mMDP & RL: Value Iteration and Policy Iteration4mMDP & RL: Action Value Function9mOptions and Option pricing7mBlack-Scholes-Merton (BSM) Model8mBSM Model and Risk9mDiscrete Time BSM Model7mDiscrete Time BSM Hedging and Pricing8mDiscrete Time BSM BS Limit6m

2 readings

Jupyter Notebook FAQ10mHedged Monte Carlo: low variance derivative pricing with objective probabilities10m

Week2

Week 2

4 hours to complete

MDP model for option pricing: Dynamic Programming Approach

4 hours to complete

7 videos (Total 59 min), 2 readings, 1 quiz

Week3

Week 3

4 hours to complete

MDP model for option pricing - Reinforcement Learning approach

4 hours to complete

8 videos (Total 71 min), 3 readings, 1 quiz

Week4

Week 4

5 hours to complete

RL and INVERSE RL for Portfolio Stock Trading

5 hours to complete

10 videos (Total 82 min), 2 readings, 1 quiz

Reviews

3.6

34 reviews

5 stars
49.19%
4 stars
14.51%
3 stars
4.03%
2 stars
9.67%
1 star
22.58%

TOP REVIEWS FROM REINFORCEMENT LEARNING IN FINANCE

by LCJun 5, 2019

Excellent course. The peer reviewed evaluation is very interisting and it is definitely worth the time to do it in detail but does not take two hours with luck a week.

by SMJun 23, 2021

Challenging course as a non-finance person, but learned a lot.

View all reviews

About the Machine Learning and Reinforcement Learning in Finance Specialization

The main goal of this specialization is to provide the knowledge and practical skills necessary to develop a strong foundation on core paradigms and algorithms of machine learning (ML), with a particular focus on applications of ML to various practical problems in Finance.

The specialization aims at helping students to be able to solve practical ML-amenable problems that they may encounter in real life that include: (1) mapping the problem on a general landscape of available ML methods, (2) choosing particular ML approach(es) that would be most appropriate for resolving the problem, and (3) successfully implementing a solution, and assessing its performance. The specialization is designed for three categories of students: · Practitioners working at financial institutions such as banks, asset management firms or hedge funds · Individuals interested in applications of ML for personal day trading · Current full-time students pursuing a degree in Finance, Statistics, Computer Science, Mathematics, Physics, Engineering or other related disciplines who want to learn about practical applications of ML in Finance. The modules can also be taken individually to improve relevant skills in a particular area of applications of ML to finance.

Machine Learning and Reinforcement Learning in Finance

Frequently Asked Questions

When will I have access to the lectures and assignments?
Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:
The course may not offer an audit option. You can try a Free Trial instead, or apply for Financial Aid.
The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
What will I get if I subscribe to this Specialization?
When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.
Is financial aid available?
Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

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Reinforcement Learning in Finance

About this Course

Skills you will gain