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Avis et commentaires pour d'étudiants pour Overview of Advanced Methods of Reinforcement Learning in Finance par New York University

3.8
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76 évaluations

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 - 12 sur 12 Avis pour Overview of Advanced Methods of Reinforcement Learning in Finance

par Teemu P

16 mars 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 Wei X

28 mars 2020

Contents of Week1 and Week4 are really useful, as the instructor recommended several academic papers on relevant topics. However the instructor failed to expand them, at least will be helpful to outline the basic ideas of each paper. The instructor only mentioned the authors' names and paper title. It's a pity.

However, week 2 and week 3 are totally useless in understanding finance and reinforcement learning. It's just a pile of formulas from physics, not interesting or pertinent to course topic at all. Moreover there is a strange signal term in the drift of stochastic process. I don't think anyone in industry is ever using this less-known dynamic to pricing or trading.

It's definitely better that Week2 and Week3 could be removed completely and be replaced by expansions of the academic papers that the instructor recommended.

par Matthieu B

29 sept. 2018

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

par Ehsan F

16 mars 2020

Never have wasted my time on anything as useless as this one! If I wanted to go read the book to learn and take the exam I wouldn't need you. Just don't take this course Or any of the courses on this specialization.

par Yi W

15 mai 2022

When I got down to the course 4, I completely collapsed as it almost had nothing to do with "reinforcement learning". The lectures almost have nothing to do with the core of this specializaiton "reinforcement learning". The project has nothing to do with Reinforcement learning, it is to use MLE to estimate parameters of a model proposed in the paper of the instructor.

I am really pissed off by taking this specialization. I thought I would learn something, but it turened out a complete waste of my one-month time.

BTW, if you wanted to learn quantum mechanics, this is the course.

par Ishrit T

8 déc. 2019

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

par Niklas O

15 oct. 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.

par Daria Y

12 déc. 2019

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

par Rodrigo A d S

31 mai 2019

Excellent course!!!

par Luis A

28 sept. 2019

Great course.

par Abdelrahman T A

26 janv. 2020

Thanks

par Kenneth N

26 juil. 2022

Great course. You require lot of patience to complete the course. Uses lot of unnecessary history, symbols and equations to explain simple concepts. Overall it is a good overview of the big picture of RL in finance provided if u can withstand the assault of excessive mindless symbols and equations.