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Overview of Advanced Methods of Reinforcement Learning in Finance(으)로 돌아가기

뉴욕 대학교 공과 대학의 Overview of Advanced Methods of Reinforcement Learning in Finance 학습자 리뷰 및 피드백

52개의 평가
8개의 리뷰

강좌 소개

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....

최상위 리뷰

필터링 기준:

Overview of Advanced Methods of Reinforcement Learning in Finance의 7개 리뷰 중 1~7

교육 기관: Teemu P

Mar 17, 2019

Assessments are once again out of touch with the materials that have been presented and do not reflect any practical uses you may need to work on in the industry. Skip this certificate until fixed.

교육 기관: Matthieu B

Sep 29, 2018

No real follow up by the team, and the assignments have nothing to do with the classes.

교육 기관: Daria

Dec 12, 2019

Great refreshment on Stochastic calculus and overall rewind of the specialization!

교육 기관: Rodrigo A d S

Jun 01, 2019

Excellent course!!!

교육 기관: Luis A A C

Sep 28, 2019

Great course.

교육 기관: Ishrit T

Dec 09, 2019

It was very difficult to get the peer-graded assignments graded.

교육 기관: Niklas O

Oct 15, 2018

Interesting deep dive into a RL application in Finance at forefront of research, however be prepared for challenging project assignments with limited support or guidance. Not for the fainthearted.