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学生对 New York University 提供的 Reinforcement Learning in Finance 的评价和反馈

3.6
118 个评分
31 条评论

课程概述

This course aims at introducing the fundamental concepts of Reinforcement Learning (RL), and develop use cases for applications of RL for option valuation, trading, and asset management. By the end of this course, students will be able to - Use reinforcement learning to solve classical problems of Finance such as portfolio optimization, optimal trading, and option pricing and risk management. - Practice on valuable examples such as famous Q-learning using financial problems. - Apply their knowledge acquired in the course to a simple model for market dynamics that is obtained using reinforcement learning as the course project. Prerequisites are the courses "Guided Tour of Machine Learning in Finance" and "Fundamentals of Machine Learning in Finance". Students are expected to know the lognormal process and how it can be simulated. Knowledge of option pricing is not assumed but desirable....

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26 - Reinforcement Learning in Finance 的 29 个评论(共 29 个)

创建者 Yi W

May 10, 2022

Sorry but the lecture is way too lofty and not much essential. The project has nothing to do with the core of the "reinforcement learning" and it is just a MLE to verify a model of the instructor's paper. really not a good course.

创建者 Charl M

Nov 23, 2020

There is too much focus on quantitative financial analysis, and not enough time on explaining RL. Simple financial examples are beneficial to understand the practical use of RL in finance, but I felt that this course had a strong bias towards finance. I feel I still need to do further reading in order to apply RL in my work. The assignment did not supply all the data required to perform the tasks. The peer grading process did not work, we had to rely on sharing links in the discussion section to get our assignments graded.

创建者 Abdelrahman T A

Jan 26, 2020

Thank you ...and i wish all student sucess

创建者 吕子赳

Aug 25, 2021

T​he accent of the professor is completely a disaster !!!! Never try this course unless you are confident that you can learn every thing just by reading matrials of this course. Believe me