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高级

完成时间大约为14 小时

英语(English)

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可分享的证书

完成后获得证书

100% 在线

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可灵活调整截止日期

根据您的日程表重置截止日期。

高级

完成时间大约为14 小时

英语(English)

字幕:英语(English)

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New York University 徽标

New York University

教学大纲 - 您将从这门课程中学到什么

1

1

完成时间为 4 小时

Black-Scholes-Merton model, Physics and Reinforcement Learning

完成时间为 4 小时
13 个视频 (总计 103 分钟)
13 个视频
Specialization Prerequisites7分钟
Interview with Rossen Roussev14分钟
Reinforcement Learning and Ptolemy's Epicycles5分钟
PDEs in Physics and Finance5分钟
Competitive Market Equilibrium Models in Finance5分钟
I Certainly Hope You Are Wrong, Herr Professor!7分钟
Risk as a Science of Fluctuation3分钟
Markets and the Heat Death of the Universe3分钟
Option Trading and RL14分钟
Liquidity9分钟
Modeling Market Frictions9分钟
Modeling Feedback Frictions10分钟
1 个练习
Assignment 12小时
2

2

完成时间为 3 小时

Reinforcement Learning for Optimal Trading and Market Modeling

完成时间为 3 小时
8 个视频 (总计 73 分钟)
8 个视频
Invisible Hand5分钟
GBM and Its Problems9分钟
The GBM Model: An Unbounded Growth Without Defaults9分钟
Dynamics with Saturation: The Verhulst Model7分钟
The Singularity is Near9分钟
What are Defaults?11分钟
Quantum Equilibrium-Disequilibrium11分钟
1 个练习
Assignment 22小时
3

3

完成时间为 3 小时

Perception - Beyond Reinforcement Learning

完成时间为 3 小时
8 个视频 (总计 60 分钟)
8 个视频
Welcome!!4分钟
Market Dynamics and IRL5分钟
Diffusion in a Potential: The Langevin Equation8分钟
Classical Dynamics7分钟
Potential Minima and Newton's Law4分钟
Classical Dynamics: the Lagrangian and the Hamiltonian7分钟
Langevin Equation and Fokker-Planck Equations9分钟
The Fokker-Planck Equation and Quantum Mechanics12分钟
1 个练习
Assignment 32小时
4

4

完成时间为 4 小时

Other Applications of Reinforcement Learning: P-2-P Lending, Cryptocurrency, etc.

完成时间为 4 小时
9 个视频 (总计 79 分钟)
9 个视频
Welcome!!1分钟
Electronic Markets and LOB9分钟
Trades, Quotes and Order Flow7分钟
Limit Order Book8分钟
LOB Modeling8分钟
LOB Statistical Modeling10分钟
LOB Modeling with ML and RL9分钟
Other Applications of RL7分钟
The Value of Universatility15分钟

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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