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New York University

강의 계획 - 이 강좌에서 배울 내용

1

1

완료하는 데 4시간 필요

Black-Scholes-Merton model, Physics and Reinforcement Learning

완료하는 데 4시간 필요
13개 동영상 (총 103분)
13개의 동영상
Specialization Prerequisites7m
Interview with Rossen Roussev14m
Reinforcement Learning and Ptolemy's Epicycles5m
PDEs in Physics and Finance5m
Competitive Market Equilibrium Models in Finance5m
I Certainly Hope You Are Wrong, Herr Professor!7m
Risk as a Science of Fluctuation3m
Markets and the Heat Death of the Universe3m
Option Trading and RL14m
Liquidity9m
Modeling Market Frictions9m
Modeling Feedback Frictions10m
1개 연습문제
Assignment 12시간
2

2

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Reinforcement Learning for Optimal Trading and Market Modeling

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8개 동영상 (총 73분)
8개의 동영상
Invisible Hand5m
GBM and Its Problems9m
The GBM Model: An Unbounded Growth Without Defaults9m
Dynamics with Saturation: The Verhulst Model7m
The Singularity is Near9m
What are Defaults?11m
Quantum Equilibrium-Disequilibrium11m
1개 연습문제
Assignment 22시간
3

3

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Perception - Beyond Reinforcement Learning

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8개 동영상 (총 60분)
8개의 동영상
Market Dynamics and IRL5m
Diffusion in a Potential: The Langevin Equation8m
Classical Dynamics7m
Potential Minima and Newton's Law4m
Classical Dynamics: the Lagrangian and the Hamiltonian7m
Langevin Equation and Fokker-Planck Equations9m
The Fokker-Planck Equation and Quantum Mechanics12m
1개 연습문제
Assignment 32시간
4

4

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

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9개 동영상 (총 79분)
9개의 동영상
Electronic Markets and LOB9m
Trades, Quotes and Order Flow7m
Limit Order Book8m
LOB Modeling8m
LOB Statistical Modeling10m
LOB Modeling with ML and RL9m
Other Applications of RL7m
The Value of Universatility15m

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OVERVIEW OF ADVANCED METHODS OF REINFORCEMENT LEARNING IN FINANCE의 최상위 리뷰

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Machine Learning and Reinforcement Learning in Finance 특화 과정 정보

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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