Об этом курсе: 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.
Для кого этот курс: 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.
Преподаватели: Igor Halperin
| Основные сведения | |
| Уровень | Начинающий |
| Язык | English |
| Как пройти курс | Чтобы пройти курс, выполните все оцениваемые задания. |
| Оценки пользователей |
- Видео: Specialization Objectives
- Видео: Specialization Prerequisites
- Видео: Artificial Intelligence and Machine Learning, Part I
- Видео: Artificial Intelligence and Machine Learning, Part II
- Видео: Machine Learning as a Foundation of Artificial Intelligence, Part I
- Видео: Machine Learning as a Foundation of Artificial Intelligence, Part II
- Видео: Machine Learning as a Foundation of Artificial Intelligence, Part III
- Видео: Machine Learning in Finance vs Machine Learning in Tech, Part I
- Видео: Machine Learning in Finance vs Machine Learning in Tech, Part II
- Видео: 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
- Видео: The No Free Lunch Theorem
- Видео: Overfitting and Model Capacity
- Видео: Linear Regression
- Видео: Regularization, Validation Set, and Hyper-parameters
- Видео: Overview of the Supervised Machine Learning in Finance
- Видео: DataFlow and TensorFlow
- Видео: A First Demo of TensorFlow
- Видео: 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
- Видео: Gradient Descent Optimization
- Видео: Gradient Descent for Neural Networks
- Видео: 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
- Видео: Fundamental Analysis
- Видео: Machine Learning as Model Estimation
- Видео: Maximum Likelihood Estimation
- Видео: Probabilistic Classification Models
- Видео: Logistic Regression for Modeling Bank Failures, Part I
- Видео: Logistic Regression for Modeling Bank Failures, Part II
- Видео: Logistic Regression for Modeling Bank Failures, Part III
- Видео: 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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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