课程信息
3.5
83 个评分
19 个审阅
100% 在线

100% 在线

立即开始,按照自己的计划学习。
可灵活调整截止日期

可灵活调整截止日期

根据您的日程表重置截止日期。
中级

中级

完成时间(小时)

完成时间大约为16 小时

建议:9 hours/week...
可选语言

英语(English)

字幕:英语(English)
100% 在线

100% 在线

立即开始,按照自己的计划学习。
可灵活调整截止日期

可灵活调整截止日期

根据您的日程表重置截止日期。
中级

中级

完成时间(小时)

完成时间大约为16 小时

建议:9 hours/week...
可选语言

英语(English)

字幕:英语(English)

教学大纲 - 您将从这门课程中学到什么

1
完成时间(小时)
完成时间为 5 小时

Fundamentals of Supervised Learning in Finance

...
Reading
9 个视频 (总计 71 分钟), 4 个阅读材料, 1 个测验
Video9 个视频
Introduction to Fundamentals of Machine Learning in Finance4分钟
Support Vector Machines, Part 18分钟
Support Vector Machines, Part 27分钟
SVM. The Kernel Trick8分钟
Example: SVM for Prediction of Credit Spreads9分钟
Tree Methods. CART Trees9分钟
Tree Methods: Random Forests8分钟
Tree Methods: Boosting9分钟
Reading4 个阅读材料
A. Smola and B. Scholkopf, “A Tutorial on Support Vector Regression”, Statistics and Computing, vol. 14, pp. 199-229, 200415分钟
A. Geron, “Hands-On Machine Learning with Scikit-Learn and TensorFlow”, Chapters 6 & 730分钟
K. Murphy, “Machine Learning: A Probabilistic Perspective”, MIT Press, 2009, Chapter 16.415分钟
Jupyter Notebook FAQ10分钟
2
完成时间(小时)
完成时间为 4 小时

Core Concepts of Unsupervised Learning, PCA & Dimensionality Reduction

...
Reading
6 个视频 (总计 54 分钟), 3 个阅读材料, 1 个测验
Video6 个视频
PCA for Stock Returns, Part 14分钟
PCA for Stock Returns, Part 29分钟
Dimension Reduction with PCA9分钟
Dimension Reduction with tSNE11分钟
Dimension Reduction with Autoencoders9分钟
Reading3 个阅读材料
C. Bishop, “Pattern Recognition and Machine Learning”, Chapter 12.115分钟
A. Geron, “Hands-On ML”, Chapters 8 & 1530分钟
Jupyter Notebook FAQ10分钟
3
完成时间(小时)
完成时间为 4 小时

Data Visualization & Clustering

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Reading
7 个视频 (总计 50 分钟), 3 个阅读材料, 1 个测验
Video7 个视频
UL. K-clustering8分钟
UL. K-means Neural Algorithm7分钟
UL. Hierarchical Clustering Algorithms10分钟
UL. Clustering and Estimation of Equity Correlation Matrix5分钟
UL. Minimum Spanning Trees, Kruskal Algorithm6分钟
UL. Probabilistic Clustering6分钟
Reading3 个阅读材料
C. Bishop, “Pattern Recognition and Machine Learning”, Clustering and EM: Chapter 930分钟
G. Bonanno et. al. “Networks of equities in financial markets”, The European Physical Journal B, vol. 38, issue 2, pp. 363-371 (2004)15分钟
Jupyter Notebook FAQ10分钟
4
完成时间(小时)
完成时间为 5 小时

Sequence Modeling and Reinforcement Learning

...
Reading
11 个视频 (总计 101 分钟), 3 个阅读材料, 1 个测验
Video11 个视频
Sequence Modeling10分钟
SM. Latent Variables for Sequences8分钟
SM. State-Space Models9分钟
SM. Hidden Markov Models9分钟
Neural Architecture for Sequential Data12分钟
RL. Introduction8分钟
RL. Core Ideas7分钟
Markov Decision Process and RL8分钟
RL. Bellman Equation6分钟
RL and Inverse Reinforcement Learning11分钟
Reading3 个阅读材料
C. Bishop, “Pattern Recognition and Machine Learning”, Chapter 1310分钟
S. Marsland, “Machine Learning: an Algorithmic Perspective” (Chapman & Hall 2009), Chapter 1315分钟
Jupyter Notebook FAQ10分钟

关于 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....

关于 Machine Learning and Reinforcement Learning in Finance 专项课程

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

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