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Machine Learning and Reinforcement Learning in Finance Specialization

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Course Features

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Duration

4 months

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Delivery Method

Online

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Available on

Limited Access

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Accessibility

Desktop, Laptop

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Language

English

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Subtitles

English

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Level

Intermediate

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Effort

5 hours per week

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Teaching Type

Self Paced

Course Description

This specialization aims at giving students the practical knowledge, skills, and foundations necessary to create a solid foundation in core algorithms and paradigms for machine learning (ML). Particular attention is given to how machine learning can be applied in the context of finance problems. This specialization is intended to assist students in solving real ML-amenable problems that they face every day. This includes (1) mapping the problem onto an extensive landscape of available ML methods.
(2) Choosing the ML approach that is most appropriate for the problem.
(3) Successfully implementing a solution and evaluating its effectiveness.
This specialization is available to three types of students: Bank employees, asset managers, hedge funds, and financial professionals; Individuals interested in ML applications that allow for personal day trading; Full-time students currently studying Computer Science, Statistics, or Finance. To enhance your knowledge in a particular area of ML finance, you can also take individual modules.

Course Overview

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Virtual Labs

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International Faculty

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Case Based Learning

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Post Course Interactions

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Case Studies,Hands-On Training,Instructor-Moderated Discussions

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Case Studies, Captstone Projects

Skills You Will Gain

What You Will Learn

Compare ML for Finance with ML in Technology (image and speech recognition, robotics, etc)

Describe linear regression and classification models and methods of their evaluation

Explain how Reinforcement Learning is used for stock trading

Become familiar with popular approaches to modeling market frictions and feedback effects for option trading

Course Instructors

Igor Halperin

Instructor

Igor Halperin was a Research Professor of Financial Machine Learning at NYU Tandon School of Engineering. His research focuses on using methods of Reinforcement Learning, Information Theory, neurosci...
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