À propos de ce cours : 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.
Les destinataires de ce cours : 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.
Enseigné par : Igor Halperin
| Informations de base | Cours 1 de 4 dans Machine Learning and Reinforcement Learning in Finance Specialization |
| Niveau | Débutant |
| Langue | English |
| Comment réussir | Réussissez tous les devoirs notés pour terminer le cours. |
| Notes des utilisateurs |
- Vidéo: Specialization Objectives
- Vidéo: Specialization Prerequisites
- Vidéo: Artificial Intelligence and Machine Learning, Part I
- Vidéo: Artificial Intelligence and Machine Learning, Part II
- Vidéo: Machine Learning as a Foundation of Artificial Intelligence, Part I
- Vidéo: Machine Learning as a Foundation of Artificial Intelligence, Part II
- Vidéo: Machine Learning as a Foundation of Artificial Intelligence, Part III
- Vidéo: Machine Learning in Finance vs Machine Learning in Tech, Part I
- Vidéo: Machine Learning in Finance vs Machine Learning in Tech, Part II
- Vidéo: Machine Learning in Finance vs Machine Learning in Tech, Part III
- Reading: The Business of Artificial Intelligence
- Reading: How AI and Automation Will Shape Finance in the Future
- Reading: A. Geron, “Hands-On Machine Learning with Scikit-Learn and TensorFlow”, Chapter 1
- Reading: Jupyter Notebook FAQ
- Notebook: Euclidean Distance Calculation
- Vidéo: The No Free Lunch Theorem
- Vidéo: Overfitting and Model Capacity
- Vidéo: Linear Regression
- Vidéo: Regularization, Validation Set, and Hyper-parameters
- Vidéo: Overview of the Supervised Machine Learning in Finance
- Vidéo: DataFlow and TensorFlow
- Vidéo: A First Demo of TensorFlow
- Vidéo: Linear Regression in TensorFlow
- Reading: I. Goodfellow, Y. Bengio, A. Courville, “Deep Learning”, Chapters 4.5, 5.1, 5.2, 5.3, 5.4
- Reading: Leo Breiman, “Statistical Modeling: The Two Cultures”
- Reading: Jupyter Notebook FAQ
- Notebook: Linear Regression
- Vidéo: Gradient Descent Optimization
- Vidéo: Gradient Descent for Neural Networks
- Vidéo: Stochastic Gradient Descent
- Reading: A.Geron, “Hands-On ML”, Chapter 9, Chapter 4 (Gradient Descent)
- Reading: 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.
- Reading: 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. 1-41
- Reading: Jupyter Notebook FAQ
- Notebook: Tobit Regression
- Vidéo: Fundamental Analysis
- Vidéo: Machine Learning as Model Estimation
- Vidéo: Maximum Likelihood Estimation
- Vidéo: Probabilistic Classification Models
- Vidéo: Logistic Regression for Modeling Bank Failures, Part I
- Vidéo: Logistic Regression for Modeling Bank Failures, Part II
- Vidéo: Logistic Regression for Modeling Bank Failures, Part III
- Vidéo: Supervised Learning: Conclusion
- Reading: C. Bishop, “Pattern Recognition and Machine Learning”, Chapters 4.1, 4.2, 4.3
- Reading: A. Geron, “Hands-On ML”, Chapters 3, Chapter 4 (Logistic Regression)
- Reading: Jupyter Notebook FAQ
- Notebook: Course Project
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| Acheter le cours | |
|---|---|
| Accéder au contenu du cours | Disponible |
| Accéder au contenu noté | Disponible |
| Recevoir une Note Finale | Disponible |
| Obtenir un Certificat de Cours partageable | Disponible |
Assignments are extremely difficult because the instructions are not clear. I understand that the act of working through the assignments is how you learn the material, however, this goes beyond that. It felt like a battle.
The course content is okay, but the programming assignments are not well designed.
very practical for me.
Very good course! Thank you, Professor Igor Halperin