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. After taking this course, students will be able to - explain fundamental concepts of finance such as market equilibrium, no arbitrage, predictability, - discuss market modeling, - Apply the methods of Reinforcement Learning to high-frequency trading, credit risk peer-to-peer lending, and cryptocurrencies trading.
This course is part of the Machine Learning and Reinforcement Learning in Finance Specialization
Overview of Advanced Methods of Reinforcement Learning in Finance
Offered By
Machine Learning and Reinforcement Learning in Finance SpecializationSyllabus - What you will learn from this course
Black-Scholes-Merton model, Physics and Reinforcement Learning
Reinforcement Learning for Optimal Trading and Market Modeling
Perception - Beyond Reinforcement Learning
Other Applications of Reinforcement Learning: P-2-P Lending, Cryptocurrency, etc.
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Great refreshment on Stochastic calculus and overall rewind of the specialization!
About New York University Tandon School of Engineering
About the Machine Learning and Reinforcement Learning in Finance Specialization
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