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.
本課程是 Machine Learning and Reinforcement Learning in Finance 專項課程 專項課程的一部分
Fundamentals of Machine Learning in Finance
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學生職業成果
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完成這些課程後已開始新的職業生涯
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中級
完成時間大約為18 小時
英語（English）
字幕：英語（English）
學生職業成果
20%
完成這些課程後已開始新的職業生涯
18%
通過此課程獲得實實在在的工作福利
可分享的證書
完成後獲得證書
100% 在線
立即開始，按照自己的計劃學習。
可靈活調整截止日期
根據您的日程表重置截止日期。
中級
完成時間大約為18 小時
英語（English）
字幕：英語（English）
提供方
纽约大学坦登工程学院
Tandon offers comprehensive courses in engineering, applied science and technology. Each course is rooted in a tradition of invention and entrepreneurship.
教學大綱 - 您將從這門課程中學到什麼
完成時間為 5 小時
Fundamentals of Supervised Learning in Finance
完成時間為 5 小時
9 個視頻 （總計 71 分鐘）, 4 個閱讀材料, 1 個測驗
9 個視頻
Introduction to Fundamentals of Machine Learning in Finance4分鐘
Support Vector Machines, Part 18分鐘
Support Vector Machines, Part 27分鐘
SVM. The Kernel Trick8分鐘
Example: SVM for Prediction of Credit Spreads9分鐘
Tree Methods. CART Trees9分鐘
Tree Methods: Random Forests8分鐘
Tree Methods: Boosting9分鐘
4 個閱讀材料
A. Smola and B. Scholkopf, “A Tutorial on Support Vector Regression”, Statistics and Computing, vol. 14, pp. 199-229, 200415分鐘
A. Geron, “Hands-On Machine Learning with Scikit-Learn and TensorFlow”, Chapters 6 & 730分鐘
K. Murphy, “Machine Learning: A Probabilistic Perspective”, MIT Press, 2009, Chapter 16.415分鐘
Jupyter Notebook FAQ10分鐘
完成時間為 4 小時
Core Concepts of Unsupervised Learning, PCA & Dimensionality Reduction
完成時間為 4 小時
6 個視頻 （總計 54 分鐘）, 3 個閱讀材料, 1 個測驗
6 個視頻
PCA for Stock Returns, Part 14分鐘
PCA for Stock Returns, Part 29分鐘
Dimension Reduction with PCA9分鐘
Dimension Reduction with tSNE11分鐘
Dimension Reduction with Autoencoders9分鐘
3 個閱讀材料
C. Bishop, “Pattern Recognition and Machine Learning”, Chapter 12.115分鐘
A. Geron, “Hands-On ML”, Chapters 8 & 1530分鐘
Jupyter Notebook FAQ10分鐘
完成時間為 4 小時
Data Visualization & Clustering
完成時間為 4 小時
7 個視頻 （總計 50 分鐘）, 3 個閱讀材料, 1 個測驗
7 個視頻
UL. K-clustering8分鐘
UL. K-means Neural Algorithm7分鐘
UL. Hierarchical Clustering Algorithms10分鐘
UL. Clustering and Estimation of Equity Correlation Matrix5分鐘
UL. Minimum Spanning Trees, Kruskal Algorithm6分鐘
UL. Probabilistic Clustering6分鐘
3 個閱讀材料
C. Bishop, “Pattern Recognition and Machine Learning”, Clustering and EM: Chapter 930分鐘
G. Bonanno et. al. “Networks of equities in financial markets”, The European Physical Journal B, vol. 38, issue 2, pp. 363-371 (2004)15分鐘
Jupyter Notebook FAQ10分鐘
完成時間為 5 小時
Sequence Modeling and Reinforcement Learning
完成時間為 5 小時
11 個視頻 （總計 101 分鐘）, 3 個閱讀材料, 1 個測驗
11 個視頻
Sequence Modeling10分鐘
SM. Latent Variables for Sequences8分鐘
SM. State-Space Models9分鐘
SM. Hidden Markov Models9分鐘
Neural Architecture for Sequential Data12分鐘
RL. Introduction8分鐘
RL. Core Ideas7分鐘
Markov Decision Process and RL8分鐘
RL. Bellman Equation6分鐘
RL and Inverse Reinforcement Learning11分鐘
3 個閱讀材料
C. Bishop, “Pattern Recognition and Machine Learning”, Chapter 1310分鐘
S. Marsland, “Machine Learning: an Algorithmic Perspective” (Chapman & Hall 2009), Chapter 1315分鐘
Jupyter Notebook FAQ10分鐘
審閱
3.8
來自FUNDAMENTALS OF MACHINE LEARNING IN FINANCE的熱門評論
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.
關於 Machine Learning and Reinforcement Learning in Finance 專項課程
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.
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