Este curso faz parte do Programa de cursos integrados Machine Learning and Reinforcement Learning in Finance
Fundamentals of Machine Learning in Finance
oferecido por
Programa de cursos integrados Machine Learning and Reinforcement Learning in FinanceNew York University Tandon School of Engineering
Informações sobre o curso
3.4
66 classificações
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.
A learner with some or no previous knowledge of Machine Learning (ML) will get to know main algorithms of Supervised and Unsupervised Learning, and Reinforcement Learning, and will be able to use ML open source Python packages to design, test, and implement ML algorithms in Finance.
Fundamentals of Machine Learning in Finance will provide more at-depth view of supervised, unsupervised, and reinforcement learning, and end up in a project on using unsupervised learning for implementing a simple portfolio trading strategy.
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.
Programa - O que você aprenderá com este curso
Week
1Fundamentals of Supervised Learning in Finance
9 vídeos (Total de 71 min), 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
Week
2Core Concepts of Unsupervised Learning, PCA & Dimensionality Reduction
6 vídeos (Total de 54 min), 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
Week
3Data Visualization & Clustering
7 vídeos (Total de 50 min), 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
Week
4Sequence Modeling and Reinforcement Learning
11 vídeos (Total de 101 min), 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
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
When will I have access to the lectures and assignments?
Once you enroll for a Certificate, you’ll have access to all videos, quizzes, and programming assignments (if applicable). Peer review assignments can only be submitted and reviewed once your session has begun. If you choose to explore the course without purchasing, you may not be able to access certain assignments.
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?
Is financial aid available?
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