This course aims at providing an introductory and broad overview of the field of ML with the focus on applications on Finance. Supervised Machine Learning methods are used in the capstone project to predict bank closures. Simultaneously, while this course can be taken as a separate course, it serves as a preview of topics that are covered in more details in subsequent modules of the specialization Machine Learning and Reinforcement Learning in Finance.
Ce cours fait partie de la Spécialisation Machine Learning and Reinforcement Learning in Finance
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Spécialisation Machine Learning and Reinforcement Learning in FinanceNew York UniversityÀ propos de ce cours
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Résultats de carrière des étudiants
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47%
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Obtenez un Certificat lorsque vous terminez
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Niveau intermédiaire
Approx. 24 heures pour terminer
Anglais
Sous-titres : Anglais, Français
Résultats de carrière des étudiants
50%
ont commencé une nouvelle carrière après avoir terminé ce cours
47%
ont bénéficié d'un avantage concret dans leur carrières grâce à ce cours
Certificat partageable
Obtenez un Certificat lorsque vous terminez
100 % en ligne
Commencez dès maintenant et apprenez aux horaires qui vous conviennent.
Cours 1 sur 4 dans le
Dates limites flexibles
Réinitialisez les dates limites selon votre disponibilité.
Niveau intermédiaire
Approx. 24 heures pour terminer
Anglais
Sous-titres : Anglais, Français
Offert par
New York University
Tandon offers comprehensive courses in engineering, applied science and technology. Each course is rooted in a tradition of invention and entrepreneurship.
Programme du cours : ce que vous apprendrez dans ce cours
3 heures pour terminer
Artificial Intelligence & Machine Learning
3 heures pour terminer
11 vidéos (Total 75 min), 3 lectures, 1 quiz
11 vidéos
Welcome Note4 min
Specialization Objectives8 min
Specialization Prerequisites7 min
Artificial Intelligence and Machine Learning, Part I6 min
Artificial Intelligence and Machine Learning, Part II7 min
Machine Learning as a Foundation of Artificial Intelligence, Part I5 min
Machine Learning as a Foundation of Artificial Intelligence, Part II7 min
Machine Learning as a Foundation of Artificial Intelligence, Part III7 min
Machine Learning in Finance vs Machine Learning in Tech, Part I6 min
Machine Learning in Finance vs Machine Learning in Tech, Part II6 min
Machine Learning in Finance vs Machine Learning in Tech, Part III8 min
3 lectures
The Business of Artificial Intelligence30 min
How AI and Automation Will Shape Finance in the Future30 min
A. Geron, “Hands-On Machine Learning with Scikit-Learn and TensorFlow”, Chapter 130 min
1 exercice pour s'entraîner
Module 1 Quiz30 min
6 heures pour terminer
Mathematical Foundations of Machine Learning
6 heures pour terminer
6 vidéos (Total 45 min), 3 lectures, 2 quiz
6 vidéos
The No Free Lunch Theorem7 min
Overfitting and Model Capacity8 min
Linear Regression7 min
Regularization, Validation Set, and Hyper-parameters10 min
Overview of the Supervised Machine Learning in Finance3 min
3 lectures
I. Goodfellow, Y. Bengio, A. Courville, “Deep Learning”, Chapters 4.5, 5.1, 5.2, 5.3, 5.41 h
Leo Breiman, “Statistical Modeling: The Two Cultures”1 h
Jupyter Notebook FAQ10 min
1 exercice pour s'entraîner
Module 2 Quiz15 min
6 heures pour terminer
Introduction to Supervised Learning
6 heures pour terminer
7 vidéos (Total 75 min), 4 lectures, 2 quiz
7 vidéos
DataFlow and TensorFlow10 min
A First Demo of TensorFlow11 min
Linear Regression in TensorFlow10 min
Neural Networks11 min
Gradient Descent Optimization10 min
Gradient Descent for Neural Networks12 min
Stochastic Gradient Descent8 min
4 lectures
A.Geron, “Hands-On ML”, Chapter 9, Chapter 4 (Gradient Descent)1 h
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 min
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 min
Jupyter Notebook FAQ10 min
1 exercice pour s'entraîner
Module 3 Quiz15 min
10 heures pour terminer
Supervised Learning in Finance
10 heures pour terminer
9 vidéos (Total 66 min), 4 lectures, 3 quiz
9 vidéos
Fundamental Analysis7 min
Machine Learning as Model Estimation8 min
Maximum Likelihood Estimation10 min
Probabilistic Classification Models6 min
Logistic Regression for Modeling Bank Failures, Part I8 min
Logistic Regression for Modeling Bank Failures, Part II5 min
Logistic Regression for Modeling Bank Failures, Part III8 min
Supervised Learning: Conclusion2 min
4 lectures
C. Bishop, “Pattern Recognition and Machine Learning”, Chapters 4.1, 4.2, 4.31 h
A. Geron, “Hands-On ML”, Chapters 3, Chapter 4 (Logistic Regression)1 h
Jupyter Notebook FAQ10 min
Jupyter Notebook FAQ10 min
1 exercice pour s'entraîner
Module 4 Quiz21 min
Avis
Meilleurs avis pour GUIDED TOUR OF MACHINE LEARNING IN FINANCE
par KD23 août 2019
Introduction of ML for Financial application with combination of Scikit learn, Statsmodels and Tensorflow with neuralnets made this class very interesting. Learned and Enjoyed lot.
par VB25 août 2018
More or less this course is good and interesting. However, homework assignments were awful. It's unclear and it's very hard to understand what is asked and how it would be graded.
par MS31 déc. 2018
Despite all the problems with the assignments and the grader this course provides really good overview ML tools and their application to finance. It's definitely worth the effort
par XZ31 juil. 2018
The course content is a mix of theory and practical stuff. One star off is due to the poor quality of programming assignment, i.e., unclear instructions and explanations.
À propos du Spécialisation Machine Learning and Reinforcement Learning in Finance
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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What will I get if I subscribe to this Specialization?
When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.
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Is financial aid available?
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