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Prazos flexíveis
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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

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New York University

Programa - O que você aprenderá com este curso

Semana
1

Semana 1

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
Semana
2

Semana 2

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
Semana
3

Semana 3

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
Semana
4

Semana 4

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

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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....
Machine Learning and Reinforcement Learning in Finance

Perguntas Frequentes – FAQ

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  • 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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