Acerca de este Curso
3.5
83 calificaciones
19 revisiones
100 % en línea

100 % en línea

Comienza de inmediato y aprende a tu propio ritmo.
Fechas límite flexibles

Fechas límite flexibles

Restablece las fechas límite en función de tus horarios.
Nivel intermedio

Nivel intermedio

Horas para completar

Aprox. 16 horas para completar

Sugerido: 9 hours/week...
Idiomas disponibles

Inglés (English)

Subtítulos: Inglés (English)
100 % en línea

100 % en línea

Comienza de inmediato y aprende a tu propio ritmo.
Fechas límite flexibles

Fechas límite flexibles

Restablece las fechas límite en función de tus horarios.
Nivel intermedio

Nivel intermedio

Horas para completar

Aprox. 16 horas para completar

Sugerido: 9 hours/week...
Idiomas disponibles

Inglés (English)

Subtítulos: Inglés (English)

Programa - Qué aprenderás en este curso

Semana
1
Horas para completar
5 horas para completar

Fundamentals of Supervised Learning in Finance

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Reading
9 videos (Total 71 minutos), 4 readings, 1 quiz
Video9 videos
Introduction to Fundamentals of Machine Learning in Finance4m
Support Vector Machines, Part 18m
Support Vector Machines, Part 27m
SVM. The Kernel Trick8m
Example: SVM for Prediction of Credit Spreads9m
Tree Methods. CART Trees9m
Tree Methods: Random Forests8m
Tree Methods: Boosting9m
Reading4 lecturas
A. Smola and B. Scholkopf, “A Tutorial on Support Vector Regression”, Statistics and Computing, vol. 14, pp. 199-229, 200415m
A. Geron, “Hands-On Machine Learning with Scikit-Learn and TensorFlow”, Chapters 6 & 730m
K. Murphy, “Machine Learning: A Probabilistic Perspective”, MIT Press, 2009, Chapter 16.415m
Jupyter Notebook FAQ10m
Semana
2
Horas para completar
4 horas para completar

Core Concepts of Unsupervised Learning, PCA & Dimensionality Reduction

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Reading
6 videos (Total 54 minutos), 3 readings, 1 quiz
Video6 videos
PCA for Stock Returns, Part 14m
PCA for Stock Returns, Part 29m
Dimension Reduction with PCA9m
Dimension Reduction with tSNE11m
Dimension Reduction with Autoencoders9m
Reading3 lecturas
C. Bishop, “Pattern Recognition and Machine Learning”, Chapter 12.115m
A. Geron, “Hands-On ML”, Chapters 8 & 1530m
Jupyter Notebook FAQ10m
Semana
3
Horas para completar
4 horas para completar

Data Visualization & Clustering

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Reading
7 videos (Total 50 minutos), 3 readings, 1 quiz
Video7 videos
UL. K-clustering8m
UL. K-means Neural Algorithm7m
UL. Hierarchical Clustering Algorithms10m
UL. Clustering and Estimation of Equity Correlation Matrix5m
UL. Minimum Spanning Trees, Kruskal Algorithm6m
UL. Probabilistic Clustering6m
Reading3 lecturas
C. Bishop, “Pattern Recognition and Machine Learning”, Clustering and EM: Chapter 930m
G. Bonanno et. al. “Networks of equities in financial markets”, The European Physical Journal B, vol. 38, issue 2, pp. 363-371 (2004)15m
Jupyter Notebook FAQ10m
Semana
4
Horas para completar
5 horas para completar

Sequence Modeling and Reinforcement Learning

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Reading
11 videos (Total 101 minutos), 3 readings, 1 quiz
Video11 videos
Sequence Modeling10m
SM. Latent Variables for Sequences8m
SM. State-Space Models9m
SM. Hidden Markov Models9m
Neural Architecture for Sequential Data12m
RL. Introduction8m
RL. Core Ideas7m
Markov Decision Process and RL8m
RL. Bellman Equation6m
RL and Inverse Reinforcement Learning11m
Reading3 lecturas
C. Bishop, “Pattern Recognition and Machine Learning”, Chapter 1310m
S. Marsland, “Machine Learning: an Algorithmic Perspective” (Chapman & Hall 2009), Chapter 1315m
Jupyter Notebook FAQ10m

Instructor

Acerca de 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....

Acerca del programa especializado 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

Preguntas Frecuentes

  • Una vez que te inscribes para obtener un Certificado, tendrás acceso a todos los videos, cuestionarios y tareas de programación (si corresponde). Las tareas calificadas por compañeros solo pueden enviarse y revisarse una vez que haya comenzado tu sesión. Si eliges explorar el curso sin comprarlo, es posible que no puedas acceder a determinadas tareas.

  • Cuando te inscribes en un curso, obtienes acceso a todos los cursos que forman parte del Programa especializado y te darán un Certificado cuando completes el trabajo. Se añadirá tu Certificado electrónico a la página Logros. Desde allí, puedes imprimir tu Certificado o añadirlo a tu perfil de LinkedIn. Si solo quieres leer y visualizar el contenido del curso, puedes auditar el curso sin costo.

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