Machine Learning and Reinforcement Learning in Finance

Overview

The Machine Learning and Reinforcement Learning in Finance Specialization is offered by Coursera in partnership with New York University aims at helping students to be able to solve practical ML-amenable problems that they may encounter in real life that include: 

• mapping the problem on a general landscape of available ML methods, 
• choosing particular ML approach(es) that would be most appropriate for resolving the problem, and 
• 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.

Applied Learning Project

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.