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.
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
Informações sobre o curso
33,825 visualizações recentes
Resultados de carreira do aprendiz
50%
comecei uma nova carreira após concluir estes cursos
47%
consegui um benefício significativo de carreira com este curso
Resultados de carreira do aprendiz
50%
comecei uma nova carreira após concluir estes cursos
47%
consegui um benefício significativo de carreira com este curso
Certificados compartilháveis
Tenha o certificado após a conclusão
100% on-line
Comece imediatamente e aprenda em seu próprio cronograma.
Prazos flexíveis
Redefinir os prazos de acordo com sua programação.
Nível intermediário
Aprox. 21 horas para completar
Sugerido: 11 hours/week
Inglês
Legendas: Inglês, Francês
Programa - O que você aprenderá com este curso
3 horas para concluir
Artificial Intelligence & Machine Learning
3 horas para concluir
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 exercício prático
Module 1 Quiz30min
6 horas para concluir
Mathematical Foundations of Machine Learning
6 horas para concluir
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 exercício prático
Module 2 Quiz15min
6 horas para concluir
Introduction to Supervised Learning
6 horas para concluir
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 exercício prático
Module 3 Quiz15min
10 horas para concluir
Supervised Learning in Finance
10 horas para concluir
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 exercício prático
Module 4 Quiz21min
Avaliações
Principais avaliações do GUIDED TOUR OF MACHINE LEARNING IN FINANCE
por KDAug 23, 2019
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.
por VBAug 25, 2018
More or less this course is good and interesting. However, homework assignments were awful. It's unclear and it's very hard to understand what is asked and how it would be graded.
por MSDec 31, 2018
Despite all the problems with the assignments and the grader this course provides really good overview ML tools and their application to finance. It's definitely worth the effort
por XZJul 31, 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 MGJun 9, 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 HKJan 17, 2020
Great general overview of machine learning. I think the course can be re-organized to incorporate some of the theory and some coding tips as well, however.
por MPAug 10, 2018
The Lectures and given readings are very useful and it is required to read them to complete the assignments which will otherwise be difficult
por CLJul 11, 2018
This will be a 5 star course when all of the technical issues are resolved. More timely feedback from the staff is desirable as well.
por MJNov 16, 2019
The coding part could have been better explained and the reasoning for what is being done should be included in the coding videos.
por ABMay 27, 2018
Exceptional disposition and lucid explanations! Ideal for a Risk Management professional to sharpen machine learning skills!
por ITJun 15, 2019
A more detailed introduction and guide to python for machine learning would have made this course one of the best out there
por FBNov 5, 2019
Fantastic lectures, great first programming assignments with unfortunate tail quality of the programming assignments
por RSAug 29, 2019
Great course! Relevant concepts are described in the videos and the bibliography is accurate to cover the rest.
por COApr 18, 2019
Very Difficult - Impossible to succeed without very strong prior experience. Would deserve more guidelines
por SAFeb 28, 2020
The course is easy to understand and give insightful details on how to apply machine learning in finance
por DWSep 8, 2019
Leans heavily on explaining differences between tech and finance applications of ML, but still great!
por SSMar 17, 2019
Excellent. I picked up quite a bit of ML as applied to finance through this fast paced course.
por AWSep 12, 2018
It would be great the background theory of related concept are explained in optional videos.
por LBAug 18, 2018
Audio could be better. Low recording volume makes it difficult to listen sometimes.
por ZWSep 21, 2018
The course content is okay, but the programming assignments are not well designed.
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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