Chevron Left
Volver a Overview of Advanced Methods of Reinforcement Learning in Finance

Opiniones y comentarios de aprendices correspondientes a Overview of Advanced Methods of Reinforcement Learning in Finance por parte de New York University

75 calificaciones
12 reseña

Acerca del Curso

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....

Principales reseñas

Filtrar por:

1 - 11 de 11 revisiones para Overview of Advanced Methods of Reinforcement Learning in Finance

por Teemu P

16 de mar. de 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.

por Wei X

28 de mar. de 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.

por Matthieu B

29 de sep. de 2018

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

por Ehsan F

16 de mar. de 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.

por Ishrit T

8 de dic. de 2019

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

por Niklas O

15 de oct. de 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.

por Daria

12 de dic. de 2019

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

por Rodrigo A d S

31 de may. de 2019

Excellent course!!!

por Luis A

28 de sep. de 2019

Great course.

por Abdelrahman T A

26 de ene. de 2020


por Yi W

15 de may. de 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.