Este curso forma parte de Programa especializado: Machine Learning and Reinforcement Learning in Finance

3.8
estrellas
318 calificaciones

Igor Halperin

18.296 ya inscrito

Ofrecido Por

Programa especializado: Machine Learning and Reinforcement Learning in FinanceNew York University

Acerca de este Curso

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The course aims at helping students to be able to solve practical ML-amenable problems that they may encounter in real life that include: (1) understanding where the problem one faces lands on a general landscape of available ML methods, (2) understanding which particular ML approach(es) would be most appropriate for resolving the problem, and (3) ability to successfully implement a solution, and assess its performance.  
A learner with some or no previous knowledge of Machine Learning (ML)  will get to know main algorithms of Supervised and Unsupervised Learning, and Reinforcement Learning, and will be able to use ML open source Python packages to design, test, and implement ML algorithms in Finance.
Fundamentals of Machine Learning in Finance will provide more at-depth view of supervised, unsupervised, and reinforcement learning, and end up in a project on using unsupervised learning for implementing a simple portfolio trading strategy.

The course 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  

Experience with Python (including numpy, pandas, and IPython/Jupyter notebooks), linear algebra, basic probability theory and basic calculus is necessary to complete assignments in this course.

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Curso 2 de 4 en

Programa especializado: Machine Learning and Reinforcement Learning in Finance

Nivel intermedio

Aprox. 18 horas para completar

Inglés (English)

Fechas límite flexibles

Restablece las fechas límite en función de tus horarios.

Certificado para compartir

Obtén un certificado al finalizar

100 % en línea

Comienza de inmediato y aprende a tu propio ritmo.

Curso 2 de 4 en

Programa especializado: Machine Learning and Reinforcement Learning in Finance

Nivel intermedio

Aprox. 18 horas para completar

Inglés (English)

Instructor

Calificación del instructor

4/5 (24 calificaciones)

Igor Halperin

45.216 alumnos

4 cursos

Ofrecido por

New York University

Programa - Qué aprenderás en este curso

Semana

Semana 1

5 horas para completar

Fundamentals of Supervised Learning in Finance

5 horas para completar

9 videos (Total 71 minutos), 4 lecturas, 1 cuestionario

9 videos

What is Machine Learning in Finance?5m

Introduction to Fundamentals of Machine Learning in Finance4m

Support Vector Machines, Part 18m

Support Vector Machines, Part 27m

SVM. The Kernel Trick8m

Example: SVM for Prediction of Credit Spreads9m

Tree Methods. CART Trees9m

Tree Methods: Random Forests8m

Tree Methods: Boosting9m

4 lecturas

A. Smola and B. Scholkopf, “A Tutorial on Support Vector Regression”, Statistics and Computing, vol. 14, pp. 199-229, 200415m

A. Geron, “Hands-On Machine Learning with Scikit-Learn and TensorFlow”, Chapters 6 & 730m

K. Murphy, “Machine Learning: A Probabilistic Perspective”, MIT Press, 2009, Chapter 16.415m

Jupyter Notebook FAQ10m

Semana

Semana 2

4 horas para completar

Core Concepts of Unsupervised Learning, PCA & Dimensionality Reduction

4 horas para completar

6 videos (Total 54 minutos), 3 lecturas, 1 cuestionario

Semana

Semana 3

4 horas para completar

Data Visualization & Clustering

4 horas para completar

7 videos (Total 50 minutos), 3 lecturas, 1 cuestionario

7 videos

UL. Clustering Algorithms4m

UL. K-clustering8m

UL. K-means Neural Algorithm7m

UL. Hierarchical Clustering Algorithms10m

UL. Clustering and Estimation of Equity Correlation Matrix5m

UL. Minimum Spanning Trees, Kruskal Algorithm6m

UL. Probabilistic Clustering6m

3 lecturas

C. Bishop, “Pattern Recognition and Machine Learning”, Clustering and EM: Chapter 930m

G. Bonanno et. al. “Networks of equities in financial markets”, The European Physical Journal B, vol. 38, issue 2, pp. 363-371 (2004)15m

Jupyter Notebook FAQ10m

Semana

Semana 4

5 horas para completar

Sequence Modeling and Reinforcement Learning

5 horas para completar

11 videos (Total 101 minutos), 3 lecturas, 1 cuestionario

11 videos

SM. Latent Variables7m

Sequence Modeling10m

SM. Latent Variables for Sequences8m

SM. State-Space Models9m

SM. Hidden Markov Models9m

Neural Architecture for Sequential Data12m

RL. Introduction8m

RL. Core Ideas7m

Markov Decision Process and RL8m

RL. Bellman Equation6m

RL and Inverse Reinforcement Learning11m

3 lecturas

C. Bishop, “Pattern Recognition and Machine Learning”, Chapter 1310m

S. Marsland, “Machine Learning: an Algorithmic Perspective” (Chapman & Hall 2009), Chapter 1315m

Jupyter Notebook FAQ10m

Reseñas

3.8

73 reseña

5 stars
45,31 %
4 stars
20,62 %
3 stars
14,37 %
2 stars
5,31 %
1 star
14,37 %

Principales reseñas sobre FUNDAMENTALS OF MACHINE LEARNING IN FINANCE

por SD18 de sep. de 2019

This is a great course, I strongly recommend. However, the assignments take a while to finish.

por CV24 de jul. de 2020

Great class, but don't believe the programming assignment time estimates... takes way longer!

por JJ24 de dic. de 2018

So far so good. The lecturer refers to projects of which some weren't covered in this course. So a little confusing. Takes lots of googling to finish this course.

por AT2 de sep. de 2019

Great course which covers both theories as well as practical skills in the real implementations in the financial world.

Ver todas las reseñas

Acerca de 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

¿Cuándo podré acceder a las lecciones y tareas?
El acceso a las clases y las asignaciones depende del tipo de inscripción que tengas. Si tomas un curso en modo de oyente, verás la mayoría de los materiales del curso en forma gratuita. Para acceder a asignaciones calificadas y obtener un certificado, deberás comprar la experiencia de Certificado, ya sea durante o después de participar como oyente. Si no ves la opción de oyente:
es posible que el curso no ofrezca la opción de participar como oyente. En cambio, puedes intentar con una Prueba gratis o postularte para recibir ayuda económica.
Es posible que el curso ofrezca la opción 'Curso completo, sin certificado'. Esta opción te permite ver todos los materiales del curso, enviar las evaluaciones requeridas y obtener una calificación final. También significa que no podrás comprar una experiencia de Certificado.
¿Qué recibiré si me suscribo a este Programa especializado?
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.
¿Hay ayuda económica disponible?
Sí. En ciertos programas de aprendizaje, puedes postularte para recibir ayuda económica o una beca en caso de no poder costear los gastos de la tarifa de inscripción. Si hay ayuda económica o becas disponibles para tu selección de programa de aprendizaje, verás un enlace para postularte en la página de descripción.

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Fundamentals of Machine Learning in Finance

Acerca de este Curso