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.
The goal of Guided Tour of Machine Learning in Finance is to get a sense of what Machine Learning is, what it is for and in how many different financial problems it can be applied to.
The course 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
Experience with Python (including numpy, pandas, and IPython/Jupyter notebooks), linear algebra, basic probability theory and basic calculus is necessary to complete assignments in this course.
Ce cours fait partie de la Spécialisation Machine Learning and Reinforcement Learning in Finance
Guided Tour of Machine Learning in Finance
proposé par
Spécialisation Machine Learning and Reinforcement Learning in FinanceNew York University Tandon School of Engineering
Programme du cours : ce que vous apprendrez dans ce cours
Semaine
1Artificial Intelligence & Machine Learning
11 vidéos (Total 75 min), 4 lectures, 2 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
4 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
Jupyter Notebook FAQ10 min
1 exercice pour s'entraîner
Module 1 Quiz30 min
Semaine
2Mathematical Foundations of Machine Learning
9 vidéos (Total 78 min), 3 lectures, 2 quiz
9 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
DataFlow and TensorFlow10 min
A First Demo of TensorFlow11 min
Linear Regression in TensorFlow10 min
3 lectures
I. Goodfellow, Y. Bengio, A. Courville, “Deep Learning”, Chapters 4.5, 5.1, 5.2, 5.3, 5.4s
Leo Breiman, “Statistical Modeling: The Two Cultures”s
Jupyter Notebook FAQ10 min
1 exercice pour s'entraîner
Module 2 Quiz15 min
Semaine
3Introduction to Supervised Learning
4 vidéos (Total 43 min), 4 lectures, 2 quiz
4 vidéos
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)s
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
Semaine
4Supervised Learning in Finance
9 vidéos (Total 66 min), 3 lectures, 2 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
3 lectures
C. Bishop, “Pattern Recognition and Machine Learning”, Chapters 4.1, 4.2, 4.3s
A. Geron, “Hands-On ML”, Chapters 3, Chapter 4 (Logistic Regression)s
Jupyter Notebook FAQ10 min
1 exercice pour s'entraîner
Module 4 Quiz21 min
3.6
82 avisMeilleurs avis
par AB•May 28th 2018
Exceptional disposition and lucid explanations! Ideal for a Risk Management professional to sharpen machine learning skills!
par LB•Aug 19th 2018
Audio could be better. Low recording volume makes it difficult to listen sometimes.
À propos de 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.
À propos de la 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.
Foire Aux Questions
Quand aurai-je accès aux vidéos de cours et aux devoirs ?
Une fois que vous êtes inscrit(e) pour un Certificat, vous pouvez accéder à toutes les vidéos de cours, et à tous les quiz et exercices de programmation (le cas échéant). Vous pouvez soumettre des devoirs à examiner par vos pairs et en examiner vous-même uniquement après le début de votre session. Si vous préférez explorer le cours sans l'acheter, vous ne serez peut-être pas en mesure d'accéder à certains devoirs.
À quoi ai-je droit si je m'abonne à cette Spécialisation ?
Lorsque vous vous inscrivez au cours, vous bénéficiez d'un accès à tous les cours de la Spécialisation, et vous obtenez un Certificat lorsque vous avez réussi. Votre Certificat électronique est alors ajouté à votre page Accomplissements. À partir de cette page, vous pouvez imprimer votre Certificat ou l'ajouter à votre profil LinkedIn. Si vous souhaitez seulement lire et visualiser le contenu du cours, vous pouvez accéder gratuitement au cours en tant qu'auditeur libre.
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