Informações sobre o curso: 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.
Para quem é direcionado este curso: 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.
Ministrado por: Igor Halperin
| Informações básicas | |
| Nível | Básico |
| Idioma | English |
| Como ser aprovado | Seja aprovado em todas as tarefas para concluir o curso. |
| Classificação do usuário |
- Vídeo: Specialization Objectives
- Vídeo: Specialization Prerequisites
- Vídeo: Artificial Intelligence and Machine Learning, Part I
- Vídeo: Artificial Intelligence and Machine Learning, Part II
- Vídeo: Machine Learning as a Foundation of Artificial Intelligence, Part I
- Vídeo: Machine Learning as a Foundation of Artificial Intelligence, Part II
- Vídeo: Machine Learning as a Foundation of Artificial Intelligence, Part III
- Vídeo: Machine Learning in Finance vs Machine Learning in Tech, Part I
- Vídeo: Machine Learning in Finance vs Machine Learning in Tech, Part II
- Vídeo: Machine Learning in Finance vs Machine Learning in Tech, Part III
- 阅读: The Business of Artificial Intelligence
- 阅读: How AI and Automation Will Shape Finance in the Future
- 阅读: A. Geron, “Hands-On Machine Learning with Scikit-Learn and TensorFlow”, Chapter 1
- 阅读: Jupyter Notebook FAQ
- 笔记本: Euclidean Distance Calculation
- Vídeo: The No Free Lunch Theorem
- Vídeo: Overfitting and Model Capacity
- Vídeo: Linear Regression
- Vídeo: Regularization, Validation Set, and Hyper-parameters
- Vídeo: Overview of the Supervised Machine Learning in Finance
- Vídeo: DataFlow and TensorFlow
- Vídeo: A First Demo of TensorFlow
- Vídeo: Linear Regression in TensorFlow
- 阅读: I. Goodfellow, Y. Bengio, A. Courville, “Deep Learning”, Chapters 4.5, 5.1, 5.2, 5.3, 5.4
- 阅读: Leo Breiman, “Statistical Modeling: The Two Cultures”
- 阅读: Jupyter Notebook FAQ
- 笔记本: Linear Regression
- Vídeo: Gradient Descent Optimization
- Vídeo: Gradient Descent for Neural Networks
- Vídeo: Stochastic Gradient Descent
- 阅读: A.Geron, “Hands-On ML”, Chapter 9, Chapter 4 (Gradient Descent)
- 阅读: 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.
- 阅读: 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
- 阅读: Jupyter Notebook FAQ
- 笔记本: Tobit Regression
- Vídeo: Fundamental Analysis
- Vídeo: Machine Learning as Model Estimation
- Vídeo: Maximum Likelihood Estimation
- Vídeo: Probabilistic Classification Models
- Vídeo: Logistic Regression for Modeling Bank Failures, Part I
- Vídeo: Logistic Regression for Modeling Bank Failures, Part II
- Vídeo: Logistic Regression for Modeling Bank Failures, Part III
- Vídeo: Supervised Learning: Conclusion
- 阅读: C. Bishop, “Pattern Recognition and Machine Learning”, Chapters 4.1, 4.2, 4.3
- 阅读: A. Geron, “Hands-On ML”, Chapters 3, Chapter 4 (Logistic Regression)
- 阅读: Jupyter Notebook FAQ
- 笔记本: Course Project
每门课程都像是一本互动的教科书，具有预先录制的视频、测验和项目。
与其他成千上万的学生相联系，对想法进行辩论，讨论课程材料，并寻求帮助来掌握概念。
获得正式认证的作业，并与朋友、同事和雇主分享您的成功。
| Comprar curso | |
|---|---|
| Acesso aos materiais do curso | Disponível |
| Acesso a materiais valendo nota | Disponível |
| Receba uma nota final | Disponível |
| Obtenha um Certificado de Curso compartilhável | Disponível |
Amazing Course!
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.
This is an excellent course bringing together machine learning and finance. The content and exercises are just nice as introduction to both subjects. The clarity of contents presented in relating these 2 are timely and commendable. The Jupyter notebooks were a little buggy with some annoying glitches in the beginning but things are all ok. The descriptions in Jupyter on what the students need to achieve probably need a bit of polish. Overall a 5-star. Great job to Professor Halperin and team.
The Lectures and given readings are very useful and it is required to read them to complete the assignments which will otherwise be difficult