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

3.8
estrellas
648 calificaciones

83 %

Igor Halperin

30.543 ya inscrito

Ofrecido Por

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

Acerca de este Curso

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This course aims at providing an introductory and broad overview of the field of ML with the focus on applications on Finance. Supervised Machine Learning methods are used in the capstone project to predict bank closures. Simultaneously, while this course can be taken as a separate course, it serves as a preview of topics that are covered in more details in subsequent modules of the specialization Machine Learning and Reinforcement Learning in Finance.
The goal  of Guided Tour of Machine Learning in Finance is to get a sense of what Machine Learning is, what it is for and in how many different financial problems it can be applied to.

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.

Fechas límite flexibles

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Certificado para compartir

Obtén un certificado al finalizar

100 % en línea

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Laboratorios de Coursera

Incluye proyectos de aprendizaje prácticos.

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Curso 1 de 4 en

Programa especializado: Machine Learning and Reinforcement Learning in Finance

Nivel intermedio

Aprox. 24 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.

Laboratorios de Coursera

Incluye proyectos de aprendizaje prácticos.

Obtén más información sobre los laboratorios de Coursera

Curso 1 de 4 en

Programa especializado: Machine Learning and Reinforcement Learning in Finance

Nivel intermedio

Aprox. 24 horas para completar

Inglés (English)

Instructor

Calificación del instructor

3,9/5 (70 calificaciones)

Igor Halperin

45.942 alumnos

4 cursos

Ofrecido por

New York University

Programa - Qué aprenderás en este curso

Calificación del contenido83%(1,750 calificaciones)

Semana1

Semana 1

3 horas para completar

Artificial Intelligence & Machine Learning

3 horas para completar

11 videos (Total 75 minutos), 3 lecturas, 1 cuestionario

11 videos

Welcome Note4mSpecialization Objectives8mSpecialization Prerequisites7mArtificial Intelligence and Machine Learning, Part I6mArtificial Intelligence and Machine Learning, Part II7mMachine Learning as a Foundation of Artificial Intelligence, Part I5mMachine Learning as a Foundation of Artificial Intelligence, Part II7mMachine Learning as a Foundation of Artificial Intelligence, Part III7mMachine Learning in Finance vs Machine Learning in Tech, Part I6mMachine Learning in Finance vs Machine Learning in Tech, Part II6mMachine Learning in Finance vs Machine Learning in Tech, Part III8m

3 lecturas

The Business of Artificial Intelligence30mHow AI and Automation Will Shape Finance in the Future30mA. Geron, “Hands-On Machine Learning with Scikit-Learn and TensorFlow”, Chapter 130m

1 ejercicio de práctica

Module 1 Quiz30m

Semana2

Semana 2

6 horas para completar

Mathematical Foundations of Machine Learning

6 horas para completar

6 videos (Total 45 minutos), 3 lecturas, 2 cuestionarios

6 videos

Generalization and a Bias-Variance Tradeoff6mThe No Free Lunch Theorem7mOverfitting and Model Capacity8mLinear Regression7mRegularization, Validation Set, and Hyper-parameters10mOverview of the Supervised Machine Learning in Finance3m

3 lecturas

I. Goodfellow, Y. Bengio, A. Courville, “Deep Learning”, Chapters 4.5, 5.1, 5.2, 5.3, 5.41hLeo Breiman, “Statistical Modeling: The Two Cultures”1hJupyter Notebook FAQ10m

1 ejercicio de práctica

Module 2 Quiz15m

Semana3

Semana 3

6 horas para completar

Introduction to Supervised Learning

6 horas para completar

7 videos (Total 75 minutos), 4 lecturas, 2 cuestionarios

7 videos

DataFlow and TensorFlow10mA First Demo of TensorFlow11mLinear Regression in TensorFlow10mNeural Networks11mGradient Descent Optimization10mGradient Descent for Neural Networks12mStochastic Gradient Descent8m

4 lecturas

A.Geron, “Hands-On ML”, Chapter 9, Chapter 4 (Gradient Descent)1hE. Fama and K. French, “Size and Book-to-Market Factors in Earnings and Returns”, Journal of Finance, vol. 50, no. 1 (1995), pp. 131-155.15mJ. Piotroski, “Value Investing: The Use of Historical Financial Statement Information to Separate Winners from Losers”, Journal of Accounting Research, Vol. 38, Supplement: Studies on Accounting Information and the Economics of the Firm (2000), pp. 1-4115mJupyter Notebook FAQ10m

1 ejercicio de práctica

Module 3 Quiz15m

Semana4

Semana 4

10 horas para completar

Supervised Learning in Finance

10 horas para completar

9 videos (Total 66 minutos), 4 lecturas, 3 cuestionarios

9 videos

Regression and Equity Analysis7mFundamental Analysis7mMachine Learning as Model Estimation8mMaximum Likelihood Estimation10mProbabilistic Classification Models6mLogistic Regression for Modeling Bank Failures, Part I8mLogistic Regression for Modeling Bank Failures, Part II5mLogistic Regression for Modeling Bank Failures, Part III8mSupervised Learning: Conclusion2m

4 lecturas

C. Bishop, “Pattern Recognition and Machine Learning”, Chapters 4.1, 4.2, 4.31hA. Geron, “Hands-On ML”, Chapters 3, Chapter 4 (Logistic Regression)1hJupyter Notebook FAQ10mJupyter Notebook FAQ10m

1 ejercicio de práctica

Module 4 Quiz21m

Reseñas

3.8

214 reseña

5 stars
41,91 %
4 stars
23,88 %
3 stars
13,71 %
2 stars
10,63 %
1 star
9,86 %

Principales reseñas sobre GUIDED TOUR OF MACHINE LEARNING IN FINANCE

por MG9 de jun. de 2019

Good material but assignments explanation were too sparse and even expectation of material not covered in videos or readings (example is Tobit regression in week 4).

por SS28 de feb. de 2020

The course is easy to understand and give insightful details on how to apply machine learning in finance

por ZX31 de jul. de 2018

The course content is a mix of theory and practical stuff. One star off is due to the poor quality of programming assignment, i.e., unclear instructions and explanations.

por DW8 de sep. de 2019

Leans heavily on explaining differences between tech and finance applications of ML, but still great!

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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Guided Tour of Machine Learning in Finance

Acerca de este Curso