课程信息
3.4
26 个评分
8 个审阅
100% 在线

100% 在线

立即开始,按照自己的计划学习。
可灵活调整截止日期

可灵活调整截止日期

根据您的日程表重置截止日期。
高级

高级

完成时间(小时)

完成时间大约为20 小时

建议:6 hours/week...
可选语言

英语(English)

字幕:英语(English)
100% 在线

100% 在线

立即开始,按照自己的计划学习。
可灵活调整截止日期

可灵活调整截止日期

根据您的日程表重置截止日期。
高级

高级

完成时间(小时)

完成时间大约为20 小时

建议:6 hours/week...
可选语言

英语(English)

字幕:英语(English)

教学大纲 - 您将从这门课程中学到什么

1
完成时间(小时)
完成时间为 4 小时

MDP and Reinforcement Learning

...
Reading
14 个视频 (总计 107 分钟), 2 个阅读材料, 1 个测验
Video14 个视频
Prerequisites7分钟
Welcome to the Course5分钟
Introduction to Markov Decision Processes and Reinforcement Learning in Finance9分钟
MDP and RL: Decision Policies9分钟
MDP & RL: Value Function and Bellman Equation7分钟
MDP & RL: Value Iteration and Policy Iteration4分钟
MDP & RL: Action Value Function9分钟
Options and Option pricing7分钟
Black-Scholes-Merton (BSM) Model8分钟
BSM Model and Risk9分钟
Discrete Time BSM Model7分钟
Discrete Time BSM Hedging and Pricing8分钟
Discrete Time BSM BS Limit6分钟
Reading2 个阅读材料
Jupyter Notebook FAQ10分钟
Hedged Monte Carlo: low variance derivative pricing with objective probabilities10分钟
2
完成时间(小时)
完成时间为 4 小时

MDP model for option pricing: Dynamic Programming Approach

...
Reading
7 个视频 (总计 59 分钟), 2 个阅读材料, 1 个测验
Video7 个视频
Action-Value Function5分钟
Optimal Action From Q Function6分钟
Backward Recursion for Q Star8分钟
Basis Functions8分钟
Optimal Hedge With Monte-Carlo8分钟
Optimal Q Function With Monte-Carlo10分钟
Reading2 个阅读材料
Jupyter Notebook FAQ10分钟
QLBS: Q-Learner in the Black-Scholes(-Merton) Worlds10分钟
3
完成时间(小时)
完成时间为 4 小时

MDP model for option pricing - Reinforcement Learning approach

...
Reading
8 个视频 (总计 71 分钟), 3 个阅读材料, 1 个测验
Video8 个视频
Batch Reinforcement Learning9分钟
Stochastic Approximations8分钟
Q-Learning8分钟
Fitted Q-Iteration10分钟
Fitted Q-Iteration: the Ψ-basis9分钟
Fitted Q-Iteration at Work11分钟
RL Solution: Discussion and Examples11分钟
Reading3 个阅读材料
Jupyter Notebook FAQ10分钟
QLBS: Q-Learner in the Black-Scholes(-Merton) Worlds and The QLBS Learner Goes NuQLear10分钟
Course Project Reading: Global Portfolio Optimization10分钟
4
完成时间(小时)
完成时间为 5 小时

RL and INVERSE RL for Portfolio Stock Trading

...
Reading
10 个视频 (总计 82 分钟), 2 个阅读材料, 1 个测验
Video10 个视频
Introduction to RL for Trading12分钟
Portfolio Model8分钟
One Period Rewards6分钟
Forward and Inverse Optimisation10分钟
Reinforcement Learning for Portfolios9分钟
Entropy Regularized RL8分钟
RL Equations10分钟
RL and Inverse Reinforcement Learning Solutions10分钟
Course Summary3分钟
Reading2 个阅读材料
Jupyter Notebook FAQ10分钟
Multi-period trading via Convex Optimization10分钟

关于 New York University Tandon School of Engineering

Tandon offers comprehensive courses in engineering, applied science and technology. Each course is rooted in a tradition of invention and entrepreneurship....

关于 Machine Learning and Reinforcement Learning in Finance 专项课程

The main goal of this specialization is to provide the knowledge and practical skills necessary to develop a strong foundation on core paradigms and algorithms of machine learning (ML), with a particular focus on applications of ML to various practical problems in Finance. The specialization aims at helping students to be able to solve practical ML-amenable problems that they may encounter in real life that include: (1) mapping the problem on a general landscape of available ML methods, (2) choosing particular ML approach(es) that would be most appropriate for resolving the problem, and (3) successfully implementing a solution, and assessing its performance. The specialization is designed for three categories of students: · Practitioners working at financial institutions such as banks, asset management firms or hedge funds · Individuals interested in applications of ML for personal day trading · Current full-time students pursuing a degree in Finance, Statistics, Computer Science, Mathematics, Physics, Engineering or other related disciplines who want to learn about practical applications of ML in Finance. The modules can also be taken individually to improve relevant skills in a particular area of applications of ML to finance....
Machine Learning and Reinforcement Learning in Finance

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