课程信息
3.6
197 个评分
82 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。
初级

初级

完成时间(小时)

完成时间大约为21 小时

建议:10 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。
初级

初级

完成时间(小时)

完成时间大约为21 小时

建议:10 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 6 小时

Artificial Intelligence & Machine Learning

...
Reading
11 个视频 (总计 75 分钟), 4 个阅读材料, 2 个测验
Video11 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
Reading4 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
Jupyter Notebook FAQ10分钟
Quiz1 个练习
Module 1 Quiz30分钟
2
完成时间(小时)
完成时间为 6 小时

Mathematical Foundations of Machine Learning

...
Reading
9 个视频 (总计 78 分钟), 3 个阅读材料, 2 个测验
Video9 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
DataFlow and TensorFlow10分钟
A First Demo of TensorFlow11分钟
Linear Regression in TensorFlow10分钟
Reading3 个阅读材料
I. Goodfellow, Y. Bengio, A. Courville, “Deep Learning”, Chapters 4.5, 5.1, 5.2, 5.3, 5.4分钟
Leo Breiman, “Statistical Modeling: The Two Cultures”分钟
Jupyter Notebook FAQ10分钟
Quiz1 个练习
Module 2 Quiz15分钟
3
完成时间(小时)
完成时间为 5 小时

Introduction to Supervised Learning

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Reading
4 个视频 (总计 43 分钟), 4 个阅读材料, 2 个测验
Video4 个视频
Gradient Descent Optimization10分钟
Gradient Descent for Neural Networks12分钟
Stochastic Gradient Descent8分钟
Reading4 个阅读材料
A.Geron, “Hands-On ML”, Chapter 9, Chapter 4 (Gradient Descent)分钟
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
Quiz1 个练习
Module 3 Quiz15分钟
4
完成时间(小时)
完成时间为 7 小时

Supervised Learning in Finance

...
Reading
9 个视频 (总计 66 分钟), 3 个阅读材料, 2 个测验
Video9 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
Reading3 个阅读材料
C. Bishop, “Pattern Recognition and Machine Learning”, Chapters 4.1, 4.2, 4.3分钟
A. Geron, “Hands-On ML”, Chapters 3, Chapter 4 (Logistic Regression)分钟
Jupyter Notebook FAQ10分钟
Quiz1 个练习
Module 4 Quiz21分钟
3.6
82 个审阅Chevron Right

热门审阅

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 LBAug 19th 2018

Audio could be better. Low recording volume makes it difficult to listen sometimes.

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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