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.
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Machine Learning and Reinforcement Learning in Finance 專項課程New York University課程信息
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高級
完成時間大約為14 小時
英語（English）
字幕：英語（English）
可分享的證書
完成後獲得證書
100% 在線
立即開始，按照自己的計劃學習。
可靈活調整截止日期
根據您的日程表重置截止日期。
高級
完成時間大約為14 小時
英語（English）
字幕：英語（English）
提供方
New York University
Tandon offers comprehensive courses in engineering, applied science and technology. Each course is rooted in a tradition of invention and entrepreneurship.
教學大綱 - 您將從這門課程中學到什麼
完成時間為 4 小時
Black-Scholes-Merton model, Physics and Reinforcement Learning
完成時間為 4 小時
13 個視頻 （總計 103 分鐘）
13 個視頻
Specialization Prerequisites7分鐘
Interview with Rossen Roussev14分鐘
Reinforcement Learning and Ptolemy's Epicycles5分鐘
PDEs in Physics and Finance5分鐘
Competitive Market Equilibrium Models in Finance5分鐘
I Certainly Hope You Are Wrong, Herr Professor!7分鐘
Risk as a Science of Fluctuation3分鐘
Markets and the Heat Death of the Universe3分鐘
Option Trading and RL14分鐘
Liquidity9分鐘
Modeling Market Frictions9分鐘
Modeling Feedback Frictions10分鐘
1 個練習
Assignment 12小時
完成時間為 3 小時
Reinforcement Learning for Optimal Trading and Market Modeling
完成時間為 3 小時
8 個視頻 （總計 73 分鐘）
8 個視頻
Invisible Hand5分鐘
GBM and Its Problems9分鐘
The GBM Model: An Unbounded Growth Without Defaults9分鐘
Dynamics with Saturation: The Verhulst Model7分鐘
The Singularity is Near9分鐘
What are Defaults?11分鐘
Quantum Equilibrium-Disequilibrium11分鐘
1 個練習
Assignment 22小時
完成時間為 3 小時
Perception - Beyond Reinforcement Learning
完成時間為 3 小時
8 個視頻 （總計 60 分鐘）
8 個視頻
Welcome!!4分鐘
Market Dynamics and IRL5分鐘
Diffusion in a Potential: The Langevin Equation8分鐘
Classical Dynamics7分鐘
Potential Minima and Newton's Law4分鐘
Classical Dynamics: the Lagrangian and the Hamiltonian7分鐘
Langevin Equation and Fokker-Planck Equations9分鐘
The Fokker-Planck Equation and Quantum Mechanics12分鐘
1 個練習
Assignment 32小時
完成時間為 4 小時
Other Applications of Reinforcement Learning: P-2-P Lending, Cryptocurrency, etc.
完成時間為 4 小時
9 個視頻 （總計 79 分鐘）
9 個視頻
Welcome!!1分鐘
Electronic Markets and LOB9分鐘
Trades, Quotes and Order Flow7分鐘
Limit Order Book8分鐘
LOB Modeling8分鐘
LOB Statistical Modeling10分鐘
LOB Modeling with ML and RL9分鐘
Other Applications of RL7分鐘
The Value of Universatility15分鐘
關於 Machine Learning and Reinforcement Learning in Finance 專項課程
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.
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What will I get if I subscribe to this Specialization?
When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.
What is the refund policy?
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Is financial aid available?
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