Acerca de este Curso
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Sugerido: 5 hours/week...

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100 % en línea

Comienza de inmediato y aprende a tu propio ritmo.

Fechas límite flexibles

Restablece las fechas límite en función de tus horarios.

Nivel avanzado

Aprox. 16 horas para completar

Sugerido: 5 hours/week...

Inglés (English)

Subtítulos: Inglés (English)

Programa - Qué aprenderás en este curso

Semana
1
4 horas para completar

Black-Scholes-Merton model, Physics and Reinforcement Learning

13 videos (Total 103 minutos), 1 quiz
13 videos
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
1 ejercicio de práctica
Assignment 12h
Semana
2
3 horas para completar

Reinforcement Learning for Optimal Trading and Market Modeling

8 videos (Total 73 minutos), 1 quiz
8 videos
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
1 ejercicio de práctica
Assignment 22h
Semana
3
3 horas para completar

Perception - Beyond Reinforcement Learning

8 videos (Total 60 minutos), 1 quiz
8 videos
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
1 ejercicio de práctica
Assignment 32h
Semana
4
4 horas para completar

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

9 videos (Total 79 minutos), 1 quiz
9 videos
Limit Order Book8m
LOB Modeling8m
LOB Statistical Modeling10m
LOB Modeling with ML and RL9m
Other Applications of RL7m
The Value of Universatility15m

Instructor

Acerca de 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....

Acerca del programa especializado 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

Preguntas Frecuentes

  • Una vez que te inscribes para obtener un Certificado, tendrás acceso a todos los videos, cuestionarios y tareas de programación (si corresponde). Las tareas calificadas por compañeros solo pueden enviarse y revisarse una vez que haya comenzado tu sesión. Si eliges explorar el curso sin comprarlo, es posible que no puedas acceder a determinadas tareas.

  • Cuando te inscribes en un curso, obtienes acceso a todos los cursos que forman parte del Programa especializado y te darán un Certificado cuando completes el trabajo. Se añadirá tu Certificado electrónico a la página Logros. Desde allí, puedes imprimir tu Certificado o añadirlo a tu perfil de LinkedIn. Si solo quieres leer y visualizar el contenido del curso, puedes auditar el curso sin costo.

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