About this Course

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Shareable Certificate
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Flexible deadlines
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Intermediate Level
Approx. 24 hours to complete
English
Subtitles: English, French

Learner Career Outcomes

50%

started a new career after completing these courses

47%

got a tangible career benefit from this course
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Flexible deadlines
Reset deadlines in accordance to your schedule.
Intermediate Level
Approx. 24 hours to complete
English
Subtitles: English, French

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New York University

Syllabus - What you will learn from this course

Content RatingThumbs Up83%(1,395 ratings)Info
Week
1

Week 1

3 hours to complete

Artificial Intelligence & Machine Learning

3 hours to complete
11 videos (Total 75 min), 3 readings, 1 quiz
11 videos
Specialization Objectives8m
Specialization Prerequisites7m
Artificial Intelligence and Machine Learning, Part I6m
Artificial Intelligence and Machine Learning, Part II7m
Machine Learning as a Foundation of Artificial Intelligence, Part I5m
Machine Learning as a Foundation of Artificial Intelligence, Part II7m
Machine Learning as a Foundation of Artificial Intelligence, Part III7m
Machine Learning in Finance vs Machine Learning in Tech, Part I6m
Machine Learning in Finance vs Machine Learning in Tech, Part II6m
Machine Learning in Finance vs Machine Learning in Tech, Part III8m
3 readings
The Business of Artificial Intelligence30m
How AI and Automation Will Shape Finance in the Future30m
A. Geron, “Hands-On Machine Learning with Scikit-Learn and TensorFlow”, Chapter 130m
1 practice exercise
Module 1 Quiz30m
Week
2

Week 2

6 hours to complete

Mathematical Foundations of Machine Learning

6 hours to complete
6 videos (Total 45 min), 3 readings, 2 quizzes
6 videos
The No Free Lunch Theorem7m
Overfitting and Model Capacity8m
Linear Regression7m
Regularization, Validation Set, and Hyper-parameters10m
Overview of the Supervised Machine Learning in Finance3m
3 readings
I. Goodfellow, Y. Bengio, A. Courville, “Deep Learning”, Chapters 4.5, 5.1, 5.2, 5.3, 5.41h
Leo Breiman, “Statistical Modeling: The Two Cultures”1h
Jupyter Notebook FAQ10m
1 practice exercise
Module 2 Quiz15m
Week
3

Week 3

6 hours to complete

Introduction to Supervised Learning

6 hours to complete
7 videos (Total 75 min), 4 readings, 2 quizzes
7 videos
A First Demo of TensorFlow11m
Linear Regression in TensorFlow10m
Neural Networks11m
Gradient Descent Optimization10m
Gradient Descent for Neural Networks12m
Stochastic Gradient Descent8m
4 readings
A.Geron, “Hands-On ML”, Chapter 9, Chapter 4 (Gradient Descent)1h
E. Fama and K. French, “Size and Book-to-Market Factors in Earnings and Returns”, Journal of Finance, vol. 50, no. 1 (1995), pp. 131-155.15m
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. 1-4115m
Jupyter Notebook FAQ10m
1 practice exercise
Module 3 Quiz15m
Week
4

Week 4

10 hours to complete

Supervised Learning in Finance

10 hours to complete
9 videos (Total 66 min), 4 readings, 3 quizzes
9 videos
Fundamental Analysis7m
Machine Learning as Model Estimation8m
Maximum Likelihood Estimation10m
Probabilistic Classification Models6m
Logistic Regression for Modeling Bank Failures, Part I8m
Logistic Regression for Modeling Bank Failures, Part II5m
Logistic Regression for Modeling Bank Failures, Part III8m
Supervised Learning: Conclusion2m
4 readings
C. Bishop, “Pattern Recognition and Machine Learning”, Chapters 4.1, 4.2, 4.31h
A. Geron, “Hands-On ML”, Chapters 3, Chapter 4 (Logistic Regression)1h
Jupyter Notebook FAQ10m
Jupyter Notebook FAQ10m
1 practice exercise
Module 4 Quiz21m

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About the Machine Learning and Reinforcement Learning in Finance Specialization

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

Frequently Asked Questions

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