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中级

完成时间大约为21 小时

建议:10 hours/week...

英语(English)

字幕:英语(English)

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21 小时

建议:10 hours/week...

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
完成时间为 3 小时

Artificial Intelligence & Machine Learning

...
11 个视频 (总计 75 分钟), 3 个阅读材料, 1 个测验
11 个视频
Specialization Objectives8分钟
Specialization Prerequisites7分钟
Artificial Intelligence and Machine Learning, Part I6分钟
Artificial Intelligence and Machine Learning, Part II7分钟
Machine Learning as a Foundation of Artificial Intelligence, Part I5分钟
Machine Learning as a Foundation of Artificial Intelligence, Part II7分钟
Machine Learning as a Foundation of Artificial Intelligence, Part III7分钟
Machine Learning in Finance vs Machine Learning in Tech, Part I6分钟
Machine Learning in Finance vs Machine Learning in Tech, Part II6分钟
Machine Learning in Finance vs Machine Learning in Tech, Part III8分钟
3 个阅读材料
The Business of Artificial Intelligence30分钟
How AI and Automation Will Shape Finance in the Future30分钟
A. Geron, “Hands-On Machine Learning with Scikit-Learn and TensorFlow”, Chapter 130分钟
1 个练习
Module 1 Quiz30分钟
2
完成时间为 6 小时

Mathematical Foundations of Machine Learning

...
6 个视频 (总计 45 分钟), 3 个阅读材料, 2 个测验
6 个视频
The No Free Lunch Theorem7分钟
Overfitting and Model Capacity8分钟
Linear Regression7分钟
Regularization, Validation Set, and Hyper-parameters10分钟
Overview of the Supervised Machine Learning in Finance3分钟
3 个阅读材料
I. Goodfellow, Y. Bengio, A. Courville, “Deep Learning”, Chapters 4.5, 5.1, 5.2, 5.3, 5.41小时
Leo Breiman, “Statistical Modeling: The Two Cultures”1小时
Jupyter Notebook FAQ10分钟
1 个练习
Module 2 Quiz15分钟
3
完成时间为 6 小时

Introduction to Supervised Learning

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7 个视频 (总计 75 分钟), 4 个阅读材料, 2 个测验
7 个视频
A First Demo of TensorFlow11分钟
Linear Regression in TensorFlow10分钟
Neural Networks11分钟
Gradient Descent Optimization10分钟
Gradient Descent for Neural Networks12分钟
Stochastic Gradient Descent8分钟
4 个阅读材料
A.Geron, “Hands-On ML”, Chapter 9, Chapter 4 (Gradient Descent)1小时
E. Fama and K. French, “Size and Book-to-Market Factors in Earnings and Returns”, Journal of Finance, vol. 50, no. 1 (1995), pp. 131-155.15分钟
J. Piotroski, “Value Investing: The Use of Historical Financial Statement Information to Separate Winners from Losers”, Journal of Accounting Research, Vol. 38, Supplement: Studies on Accounting Information and the Economics of the Firm (2000), pp. 1-4115分钟
Jupyter Notebook FAQ10分钟
1 个练习
Module 3 Quiz15分钟
4
完成时间为 10 小时

Supervised Learning in Finance

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9 个视频 (总计 66 分钟), 4 个阅读材料, 3 个测验
9 个视频
Fundamental Analysis7分钟
Machine Learning as Model Estimation8分钟
Maximum Likelihood Estimation10分钟
Probabilistic Classification Models6分钟
Logistic Regression for Modeling Bank Failures, Part I8分钟
Logistic Regression for Modeling Bank Failures, Part II5分钟
Logistic Regression for Modeling Bank Failures, Part III8分钟
Supervised Learning: Conclusion2分钟
4 个阅读材料
C. Bishop, “Pattern Recognition and Machine Learning”, Chapters 4.1, 4.2, 4.31小时
A. Geron, “Hands-On ML”, Chapters 3, Chapter 4 (Logistic Regression)1小时
Jupyter Notebook FAQ10分钟
Jupyter Notebook FAQ10分钟
1 个练习
Module 4 Quiz21分钟
3.7
103 个审阅Chevron Right

55%

完成这些课程后已开始新的职业生涯

53%

通过此课程获得实实在在的工作福利

10%

加薪或升职

来自Guided Tour of Machine Learning in Finance的热门评论

创建者 ABMay 28th 2018

Exceptional disposition and lucid explanations! Ideal for a Risk Management professional to sharpen machine learning skills!

创建者 SSMar 18th 2019

Excellent. I picked up quite a bit of ML as applied to finance through this fast paced course.

关于 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....

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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