Acerca de este Curso
3.5
145 calificaciones
66 revisiones
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....
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Cursos 100 % en línea

Comienza de inmediato y aprende a tu propio ritmo.
Calendar

Fechas límite flexibles

Restablece las fechas límite en función de tus horarios.
Beginner Level

Nivel principiante

Clock

Sugerido: 9 hours/week

Aprox. 19 horas para completar
Comment Dots

English

Subtítulos: English
Globe

Cursos 100 % en línea

Comienza de inmediato y aprende a tu propio ritmo.
Calendar

Fechas límite flexibles

Restablece las fechas límite en función de tus horarios.
Beginner Level

Nivel principiante

Clock

Sugerido: 9 hours/week

Aprox. 19 horas para completar
Comment Dots

English

Subtítulos: English

Programa - Qué aprenderás en este curso

1

Sección
Clock
6 horas para completar

Artificial Intelligence & Machine Learning

...
Reading
11 videos (Total: 75 min), 4 readings, 2 quizzes
Video11 videos
Specialization Objectives8m
Specialization Prerequisites7m
Artificial Intelligence and Machine Learning, Part I6m
Artificial Intelligence and Machine Learning, Part II7m
Machine Learning as a Foundation of Artificial Intelligence, Part I5m
Machine Learning as a Foundation of Artificial Intelligence, Part II7m
Machine Learning as a Foundation of Artificial Intelligence, Part III7m
Machine Learning in Finance vs Machine Learning in Tech, Part I6m
Machine Learning in Finance vs Machine Learning in Tech, Part II6m
Machine Learning in Finance vs Machine Learning in Tech, Part III8m
Reading4 lecturas
The Business of Artificial Intelligence30m
How AI and Automation Will Shape Finance in the Future30m
A. Geron, “Hands-On Machine Learning with Scikit-Learn and TensorFlow”, Chapter 130m
Jupyter Notebook FAQ10m
Quiz1 ejercicio de práctica
Module 1 Quiz30m

2

Sección
Clock
6 horas para completar

Mathematical Foundations of Machine Learning

...
Reading
9 videos (Total: 78 min), 3 readings, 2 quizzes
Video9 videos
The No Free Lunch Theorem7m
Overfitting and Model Capacity8m
Linear Regression7m
Regularization, Validation Set, and Hyper-parameters10m
Overview of the Supervised Machine Learning in Finance3m
DataFlow and TensorFlow10m
A First Demo of TensorFlow11m
Linear Regression in TensorFlow10m
Reading3 lecturas
I. Goodfellow, Y. Bengio, A. Courville, “Deep Learning”, Chapters 4.5, 5.1, 5.2, 5.3, 5.4m
Leo Breiman, “Statistical Modeling: The Two Cultures”m
Jupyter Notebook FAQ10m
Quiz1 ejercicio de práctica
Module 2 Quiz15m

3

Sección
Clock
5 horas para completar

Introduction to Supervised Learning

...
Reading
4 videos (Total: 43 min), 4 readings, 2 quizzes
Video4 videos
Gradient Descent Optimization10m
Gradient Descent for Neural Networks12m
Stochastic Gradient Descent8m
Reading4 lecturas
A.Geron, “Hands-On ML”, Chapter 9, Chapter 4 (Gradient Descent)m
E. Fama and K. French, “Size and Book-to-Market Factors in Earnings and Returns”, Journal of Finance, vol. 50, no. 1 (1995), pp. 131-155.15m
J. 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. 1-4115m
Jupyter Notebook FAQ10m
Quiz1 ejercicio de práctica
Module 3 Quiz15m

4

Sección
Clock
7 horas para completar

Supervised Learning in Finance

...
Reading
9 videos (Total: 66 min), 3 readings, 2 quizzes
Video9 videos
Fundamental Analysis7m
Machine Learning as Model Estimation8m
Maximum Likelihood Estimation10m
Probabilistic Classification Models6m
Logistic Regression for Modeling Bank Failures, Part I8m
Logistic Regression for Modeling Bank Failures, Part II5m
Logistic Regression for Modeling Bank Failures, Part III8m
Supervised Learning: Conclusion2m
Reading3 lecturas
C. Bishop, “Pattern Recognition and Machine Learning”, Chapters 4.1, 4.2, 4.3m
A. Geron, “Hands-On ML”, Chapters 3, Chapter 4 (Logistic Regression)m
Jupyter Notebook FAQ10m
Quiz1 ejercicio de práctica
Module 4 Quiz21m
3.5

Principales revisiones

por ABMay 28th 2018

Exceptional disposition and lucid explanations! Ideal for a Risk Management professional to sharpen machine learning skills!

por LBAug 19th 2018

Audio could be better. Low recording volume makes it difficult to listen sometimes.

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

  • Once you enroll for a Certificate, you’ll have access to all videos, quizzes, and programming assignments (if applicable). Peer review assignments can only be submitted and reviewed once your session has begun. If you choose to explore the course without purchasing, you may not be able to access certain assignments.

  • 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.

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