The course aims at helping students to be able to solve practical ML-amenable problems that they may encounter in real life that include: (1) understanding where the problem one faces lands on a general landscape of available ML methods, (2) understanding which particular ML approach(es) would be most appropriate for resolving the problem, and (3) ability to successfully implement a solution, and assess its performance.
Este curso faz parte do Programa de cursos integrados Machine Learning and Reinforcement Learning in Finance
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Informações sobre o curso
19,635 visualizações recentes
Resultados de carreira do aprendiz
20%
comecei uma nova carreira após concluir estes cursos
18%
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. 18 horas para completar
Inglês
Legendas: Inglês
Resultados de carreira do aprendiz
20%
comecei uma nova carreira após concluir estes cursos
18%
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. 18 horas para completar
Inglês
Legendas: Inglês
oferecido por
New York University
Tandon offers comprehensive courses in engineering, applied science and technology. Each course is rooted in a tradition of invention and entrepreneurship.
Programa - O que você aprenderá com este curso
5 horas para concluir
Fundamentals of Supervised Learning in Finance
5 horas para concluir
9 vídeos (Total 71 mín.), 4 leituras, 1 teste
9 videos
Introduction to Fundamentals of Machine Learning in Finance4min
Support Vector Machines, Part 18min
Support Vector Machines, Part 27min
SVM. The Kernel Trick8min
Example: SVM for Prediction of Credit Spreads9min
Tree Methods. CART Trees9min
Tree Methods: Random Forests8min
Tree Methods: Boosting9min
4 leituras
A. Smola and B. Scholkopf, “A Tutorial on Support Vector Regression”, Statistics and Computing, vol. 14, pp. 199-229, 200415min
A. Geron, “Hands-On Machine Learning with Scikit-Learn and TensorFlow”, Chapters 6 & 730min
K. Murphy, “Machine Learning: A Probabilistic Perspective”, MIT Press, 2009, Chapter 16.415min
Jupyter Notebook FAQ10min
4 horas para concluir
Core Concepts of Unsupervised Learning, PCA & Dimensionality Reduction
4 horas para concluir
6 vídeos (Total 54 mín.), 3 leituras, 1 teste
6 videos
PCA for Stock Returns, Part 14min
PCA for Stock Returns, Part 29min
Dimension Reduction with PCA9min
Dimension Reduction with tSNE11min
Dimension Reduction with Autoencoders9min
3 leituras
C. Bishop, “Pattern Recognition and Machine Learning”, Chapter 12.115min
A. Geron, “Hands-On ML”, Chapters 8 & 1530min
Jupyter Notebook FAQ10min
4 horas para concluir
Data Visualization & Clustering
4 horas para concluir
7 vídeos (Total 50 mín.), 3 leituras, 1 teste
7 videos
UL. K-clustering8min
UL. K-means Neural Algorithm7min
UL. Hierarchical Clustering Algorithms10min
UL. Clustering and Estimation of Equity Correlation Matrix5min
UL. Minimum Spanning Trees, Kruskal Algorithm6min
UL. Probabilistic Clustering6min
3 leituras
C. Bishop, “Pattern Recognition and Machine Learning”, Clustering and EM: Chapter 930min
G. Bonanno et. al. “Networks of equities in financial markets”, The European Physical Journal B, vol. 38, issue 2, pp. 363-371 (2004)15min
Jupyter Notebook FAQ10min
5 horas para concluir
Sequence Modeling and Reinforcement Learning
5 horas para concluir
11 vídeos (Total 101 mín.), 3 leituras, 1 teste
11 videos
Sequence Modeling10min
SM. Latent Variables for Sequences8min
SM. State-Space Models9min
SM. Hidden Markov Models9min
Neural Architecture for Sequential Data12min
RL. Introduction8min
RL. Core Ideas7min
Markov Decision Process and RL8min
RL. Bellman Equation6min
RL and Inverse Reinforcement Learning11min
3 leituras
C. Bishop, “Pattern Recognition and Machine Learning”, Chapter 1310min
S. Marsland, “Machine Learning: an Algorithmic Perspective” (Chapman & Hall 2009), Chapter 1315min
Jupyter Notebook FAQ10min
Avaliações
Principais avaliações do FUNDAMENTALS OF MACHINE LEARNING IN FINANCE
por JJ24 de Dez de 2018
So far so good. The lecturer refers to projects of which some weren't covered in this course. So a little confusing. Takes lots of googling to finish this course.
por AT9 de Ago de 2019
Furthered my understanding of how probabilistic models are connected to Machine Learning models. Very happy with the content in this course.
por AT2 de Set de 2019
Great course which covers both theories as well as practical skills in the real implementations in the financial world.
por AM1 de Jan de 2020
Excellent course to get ML algorithms for profit maximization approach
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
When will I have access to the lectures and assignments?
Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:
The course may not offer an audit option. You can try a Free Trial instead, or apply for Financial Aid.
The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
What will I get if I subscribe to this Specialization?
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.
What is the refund policy?
If you subscribed, you get a 7-day free trial during which you can cancel at no penalty. After that, we don’t give refunds, but you can cancel your subscription at any time. See our full refund policy.
Is financial aid available?
Yes, Coursera provides financial aid to learners who cannot afford the fee. Apply for it by clicking on the Financial Aid link beneath the "Enroll" button on the left. You'll be prompted to complete an application and will be notified if you are approved. You'll need to complete this step for each course in the Specialization, including the Capstone Project. Learn more.
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