Informações sobre o curso

11,617 visualizações recentes
Certificados compartilháveis
Tenha o certificado após a conclusão
100% on-line
Comece imediatamente e aprenda em seu próprio cronograma.
Prazos flexíveis
Redefinir os prazos de acordo com sua programação.
Nível avançado
Aprox. 14 horas para completar
Inglês
Legendas: Inglês
Certificados compartilháveis
Tenha o certificado após a conclusão
100% on-line
Comece imediatamente e aprenda em seu próprio cronograma.
Prazos flexíveis
Redefinir os prazos de acordo com sua programação.
Nível avançado
Aprox. 14 horas para completar
Inglês
Legendas: Inglês

oferecido por

Logotipo de New York University

New York University

Programa - O que você aprenderá com este curso

Semana
1

Semana 1

4 horas para concluir

Black-Scholes-Merton model, Physics and Reinforcement Learning

4 horas para concluir
13 vídeos (Total 103 mín.)
13 videos
Specialization Prerequisites7min
Interview with Rossen Roussev14min
Reinforcement Learning and Ptolemy's Epicycles5min
PDEs in Physics and Finance5min
Competitive Market Equilibrium Models in Finance5min
I Certainly Hope You Are Wrong, Herr Professor!7min
Risk as a Science of Fluctuation3min
Markets and the Heat Death of the Universe3min
Option Trading and RL14min
Liquidity9min
Modeling Market Frictions9min
Modeling Feedback Frictions10min
1 exercício prático
Assignment 12h
Semana
2

Semana 2

3 horas para concluir

Reinforcement Learning for Optimal Trading and Market Modeling

3 horas para concluir
8 vídeos (Total 73 mín.)
8 videos
Invisible Hand5min
GBM and Its Problems9min
The GBM Model: An Unbounded Growth Without Defaults9min
Dynamics with Saturation: The Verhulst Model7min
The Singularity is Near9min
What are Defaults?11min
Quantum Equilibrium-Disequilibrium11min
1 exercício prático
Assignment 22h
Semana
3

Semana 3

3 horas para concluir

Perception - Beyond Reinforcement Learning

3 horas para concluir
8 vídeos (Total 60 mín.)
8 videos
Market Dynamics and IRL5min
Diffusion in a Potential: The Langevin Equation8min
Classical Dynamics7min
Potential Minima and Newton's Law4min
Classical Dynamics: the Lagrangian and the Hamiltonian7min
Langevin Equation and Fokker-Planck Equations9min
The Fokker-Planck Equation and Quantum Mechanics12min
1 exercício prático
Assignment 32h
Semana
4

Semana 4

4 horas para concluir

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

4 horas para concluir
9 vídeos (Total 79 mín.)
9 videos
Electronic Markets and LOB9min
Trades, Quotes and Order Flow7min
Limit Order Book8min
LOB Modeling8min
LOB Statistical Modeling10min
LOB Modeling with ML and RL9min
Other Applications of RL7min
The Value of Universatility15min

Avaliações

Principais avaliações do OVERVIEW OF ADVANCED METHODS OF REINFORCEMENT LEARNING IN FINANCE

Visualizar todas as avaliações

Sobre Programa de cursos integrados Machine Learning and Reinforcement Learning in Finance

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

Perguntas Frequentes – FAQ

  • Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:

    • The course may not offer an audit option. You can try a Free Trial instead, or apply for Financial Aid.

    • The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.

  • 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.

  • If you subscribed, you get a 7-day free trial during which you can cancel at no penalty. After that, we don’t give refunds, but you can cancel your subscription at any time. See our full refund policy.

  • Yes, Coursera provides financial aid to learners who cannot afford the fee. Apply for it by clicking on the Financial Aid link beneath the "Enroll" button on the left. You'll be prompted to complete an application and will be notified if you are approved. You'll need to complete this step for each course in the Specialization, including the Capstone Project. Learn more.

Mais dúvidas? Visite o Central de Ajuda ao Aprendiz.