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.
Machine Learning and Reinforcement Learning in Finance 专项课程New York University课程信息
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学生职业成果
20%
完成这些课程后已开始新的职业生涯
17%
通过此课程获得实实在在的工作福利
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中级
完成时间大约为18 小时
英语（English）
字幕：法语（French）, （欧洲人讲的）葡萄牙语, 俄语（Russian）, 英语（English）, 西班牙语（Spanish）
学生职业成果
20%
完成这些课程后已开始新的职业生涯
17%
通过此课程获得实实在在的工作福利
可分享的证书
完成后获得证书
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
根据您的日程表重置截止日期。
中级
完成时间大约为18 小时
英语（English）
字幕：法语（French）, （欧洲人讲的）葡萄牙语, 俄语（Russian）, 英语（English）, 西班牙语（Spanish）
提供方
New York University
New York University is a leading global institution for scholarship, teaching, and research. Based in New York City with campuses and sites in 14 additional major cities across the world, NYU embraces diversity among faculty, staff and students to ensure the highest caliber, most inclusive educational experience.
教学大纲 - 您将从这门课程中学到什么
完成时间为 5 小时
Fundamentals of Supervised Learning in Finance
完成时间为 5 小时
9 个视频 （总计 71 分钟）, 4 个阅读材料, 1 个测验
9 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
4 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
完成时间为 4 小时
Core Concepts of Unsupervised Learning, PCA & Dimensionality Reduction
完成时间为 4 小时
6 个视频 （总计 54 分钟）, 3 个阅读材料, 1 个测验
6 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
3 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
完成时间为 4 小时
Data Visualization & Clustering
完成时间为 4 小时
7 个视频 （总计 50 分钟）, 3 个阅读材料, 1 个测验
7 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
3 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
完成时间为 5 小时
Sequence Modeling and Reinforcement Learning
完成时间为 5 小时
11 个视频 （总计 101 分钟）, 3 个阅读材料, 1 个测验
11 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
3 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
审阅
来自FUNDAMENTALS OF MACHINE LEARNING IN FINANCE的热门评论
由 JJ 提供Dec 24, 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.
由 AT 提供Aug 9, 2019
Furthered my understanding of how probabilistic models are connected to Machine Learning models. Very happy with the content in this course.
由 AT 提供Sep 2, 2019
Great course which covers both theories as well as practical skills in the real implementations in the financial world.
由 SD 提供Sep 18, 2019
This is a great course, I strongly recommend. However, the assignments take a while to finish.
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.
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