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 FinanceInstituto Politécnico da Universidade de Nova Iorque
Os alunos que estão fazendo este Course são
- Risk Managers
- Traders
- Data Scientists
- Machine Learning Engineers
- Economists
Programa - O que você aprenderá com este curso
Semana
13 horas para concluir
Artificial Intelligence & Machine Learning
11 vídeos (Total 75 mín.), 3 leituras, 1 teste
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
3 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
1 exercícios práticos
Module 1 Quiz30min
Semana
26 horas para concluir
Mathematical Foundations of Machine Learning
6 vídeos (Total 45 mín.), 3 leituras, 2 testes
6 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
3 leituras
I. Goodfellow, Y. Bengio, A. Courville, “Deep Learning”, Chapters 4.5, 5.1, 5.2, 5.3, 5.41h
Leo Breiman, “Statistical Modeling: The Two Cultures”1h
Jupyter Notebook FAQ10min
1 exercícios práticos
Module 2 Quiz15min
Semana
36 horas para concluir
Introduction to Supervised Learning
7 vídeos (Total 75 mín.), 4 leituras, 2 testes
7 videos
A First Demo of TensorFlow11min
Linear Regression in TensorFlow10min
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)1h
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 exercícios práticos
Module 3 Quiz15min
Semana
410 horas para concluir
Supervised Learning in Finance
9 vídeos (Total 66 mín.), 4 leituras, 3 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
4 leituras
C. Bishop, “Pattern Recognition and Machine Learning”, Chapters 4.1, 4.2, 4.31h
A. Geron, “Hands-On ML”, Chapters 3, Chapter 4 (Logistic Regression)1h
Jupyter Notebook FAQ10min
Jupyter Notebook FAQ10min
1 exercícios práticos
Module 4 Quiz21min
46%
comecei uma nova carreira após concluir estes cursos
44%
consegui um benefício significativo de carreira com este curso
Principais avaliações do Guided Tour of Machine Learning in Finance
Introduction of ML for Financial application with combination of Scikit learn, Statsmodels and Tensorflow with neuralnets made this class very interesting. Learned and Enjoyed lot.
Exceptional disposition and lucid explanations! Ideal for a Risk Management professional to sharpen machine learning skills!
Sobre Instituto Politécnico da Universidade de Nova Iorque
Tandon offers comprehensive courses in engineering, applied science and technology. Each course is rooted in a tradition of invention and entrepreneurship.
Sobre 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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