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Avis et commentaires pour d'étudiants pour Overview of Advanced Methods of Reinforcement Learning in Finance par Université de New York, Tandon School of Engineering

3.7
stars
52 évaluations
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8 avis

À propos du cours

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....

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1 - 7 sur 7 Avis pour Overview of Advanced Methods of Reinforcement Learning in Finance

par Teemu P

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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.

par Matthieu B

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Sep 29, 2018

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

par Daria

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Dec 12, 2019

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

par Rodrigo A d S

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Jun 01, 2019

Excellent course!!!

par Luis A A C

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Sep 28, 2019

Great course.

par Ishrit T

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Dec 09, 2019

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

par Niklas O

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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.

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