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.
Este curso faz parte do Programa de cursos integrados Machine Learning and Reinforcement Learning in Finance
Guided Tour of Machine Learning in Finance
oferecido por
Programa de cursos integrados Machine Learning and Reinforcement Learning in FinanceNew York University Tandon School of Engineering
Programa - O que você aprenderá com este curso
Semana
1Artificial Intelligence & Machine Learning
11 videos (Total 75 min), 4 leituras, 2 testes
11 videos
Welcome Note4min
Specialization Objectives8min
Specialization Prerequisites7min
Artificial Intelligence and Machine Learning, Part I6min
Artificial Intelligence and Machine Learning, Part II7min
Machine Learning as a Foundation of Artificial Intelligence, Part I5min
Machine Learning as a Foundation of Artificial Intelligence, Part II7min
Machine Learning as a Foundation of Artificial Intelligence, Part III7min
Machine Learning in Finance vs Machine Learning in Tech, Part I6min
Machine Learning in Finance vs Machine Learning in Tech, Part II6min
Machine Learning in Finance vs Machine Learning in Tech, Part III8min
4 leituras
The Business of Artificial Intelligence30min
How AI and Automation Will Shape Finance in the Future30min
A. Geron, “Hands-On Machine Learning with Scikit-Learn and TensorFlow”, Chapter 130min
Jupyter Notebook FAQ10min
1 exercício prático
Module 1 Quiz30min
Semana
2Mathematical Foundations of Machine Learning
9 videos (Total 78 min), 3 leituras, 2 testes
9 videos
The No Free Lunch Theorem7min
Overfitting and Model Capacity8min
Linear Regression7min
Regularization, Validation Set, and Hyper-parameters10min
Overview of the Supervised Machine Learning in Finance3min
DataFlow and TensorFlow10min
A First Demo of TensorFlow11min
Linear Regression in TensorFlow10min
3 leituras
I. Goodfellow, Y. Bengio, A. Courville, “Deep Learning”, Chapters 4.5, 5.1, 5.2, 5.3, 5.4min
Leo Breiman, “Statistical Modeling: The Two Cultures”min
Jupyter Notebook FAQ10min
1 exercício prático
Module 2 Quiz15min
Semana
3Introduction to Supervised Learning
4 videos (Total 43 min), 4 leituras, 2 testes
4 videos
Neural Networks11min
Gradient Descent Optimization10min
Gradient Descent for Neural Networks12min
Stochastic Gradient Descent8min
4 leituras
A.Geron, “Hands-On ML”, Chapter 9, Chapter 4 (Gradient Descent)min
E. Fama and K. French, “Size and Book-to-Market Factors in Earnings and Returns”, Journal of Finance, vol. 50, no. 1 (1995), pp. 131-155.15min
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. 1-4115min
Jupyter Notebook FAQ10min
1 exercício prático
Module 3 Quiz15min
Semana
4Supervised Learning in Finance
9 videos (Total 66 min), 3 leituras, 2 testes
9 videos
Fundamental Analysis7min
Machine Learning as Model Estimation8min
Maximum Likelihood Estimation10min
Probabilistic Classification Models6min
Logistic Regression for Modeling Bank Failures, Part I8min
Logistic Regression for Modeling Bank Failures, Part II5min
Logistic Regression for Modeling Bank Failures, Part III8min
Supervised Learning: Conclusion2min
3 leituras
C. Bishop, “Pattern Recognition and Machine Learning”, Chapters 4.1, 4.2, 4.3min
A. Geron, “Hands-On ML”, Chapters 3, Chapter 4 (Logistic Regression)min
Jupyter Notebook FAQ10min
1 exercício prático
Module 4 Quiz21min
Melhores avaliações
por AB•May 28th 2018
Exceptional disposition and lucid explanations! Ideal for a Risk Management professional to sharpen machine learning skills!
por LB•Aug 19th 2018
Audio could be better. Low recording volume makes it difficult to listen sometimes.
Sobre 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.
Sobre o 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.
Perguntas Frequentes – FAQ
Quando terei acesso às palestras e às tarefas?
Ao se inscrever para um Certificado, você terá acesso a todos os vídeos, testes e tarefas de programação (se aplicável). Tarefas avaliadas pelos colegas apenas podem ser enviadas e avaliadas após o início da sessão. Caso escolha explorar o curso sem adquiri-lo, talvez você não consiga acessar certas tarefas.
O que recebo ao me inscrever nesta Especialização?
Quando você se inscreve no curso, tem acesso a todos os cursos na Especialização e pode obter um certificado quando concluir o trabalho. Seu Certificado eletrônico será adicionado à sua página de Participações e você poderá imprimi-lo ou adicioná-lo ao seu perfil no LinkedIn. Se quiser apenas ler e assistir o conteúdo do curso, você poderá frequentá-lo como ouvinte sem custo.
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