Machine Learning and Reinforcement Learning in Finance Specialization

Reinforce Your Career: Machine Learning in Finance. Extend your expertise of algorithms and tools needed to predict financial markets.

About This 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.

Created by:

4 courses

Follow the suggested order or choose your own.

Projects

Designed to help you practice and apply the skills you learn.

Certificates

Highlight your new skills on your resume or LinkedIn.

Projects Overview

The specialization is essentially in ML where all examples, home assignments and course projects deal with various problems in Finance (such as stock trading, asset management, and banking applications), and the choice of topics is respectively driven by a focus on ML methods that are used by practitioners in Finance. The specialization is meant to prepare the students to work on complex machine learning projects in finance that often require both a broad understanding of the whole field of ML, and understanding of appropriateness of different methods available in a particular sub-field of ML (for example, Unsupervised Learning) for addressing practical problems they might encounter in their work.

Courses

Intermediate Specialization.

Some related experience required.

1. COURSE 1

   Guided Tour of Machine Learning in Finance

   Upcoming session: Jun 11

   Subtitles

   English

   About the Course

   This course aims at providing an introductory and broad overview of the field of ML with the focus on applications on Finance. Supervised Machine Learning methods are used in the capstone project to predict bank closures. Simultaneously, while this cours

   You can choose to take this course only. Learn more.

2. COURSE 2

   Fundamentals of Machine Learning in Finance

   Upcoming session: Jul 2

   Subtitles

   English

   About the Course

   The course aims at helping students to be able to solve practical ML-amenable problems that they may encounter in real life that include: (1) understanding where the problem one faces lands on a general landscape of available ML methods, (2) understandi

   You can choose to take this course only. Learn more.

3. COURSE 3

   Reinforcement Learning in Finance

   Current session: May 28

   Subtitles

   English

   About the Course

   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. Prerequisites are the courses "Guided Tour of Machine Learning in

   You can choose to take this course only. Learn more.

4. COURSE 4

   Overview of Advanced Methods of Reinforcement Learning in Finance

   Starts June 25th, 2018

   Subtitles

   English

   About the Course

   In the last course of our specialization, Overview of Advanced Methods of Reinforcement Learning in Finance, we will take a deeper look into topics discussed in our third course, Reinforcement Learning in Finance. In particular, we will talk about links between Reinforcement Learning, option pricing and physics, implications of Inverse Reinforcement Learning for modeling market impact and price dynamics, and perception-action cycles in Reinforcement Learning. Finally, we will overview trending and potential applications of Reinforcement Learning for high frequency trading, cryptocurrencies, peer-to-peer lending, and more.

   You can choose to take this course only. Learn more.

Creators

• 

  New York University strives to be a quality international center of scholarship, teaching and research. As one of the nation's most respected institutions, NYU Tandon School of Engineering aligns with this mission.

  Tandon offers comprehensive courses in engineering, applied science and technology. Each course is rooted in a tradition of invention and entrepreneurship.

• 

  Igor Halperin

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