Machine Learning for Finance
This course introduces fundamental machine learning techniques and their practical applications in the financial industry. This course will explore how data-driven algorithms can enhance decision-making in quantitative trading, credit risk evaluation, and fraud detection.
Lecturer
Date
17th September, 2025 ~ 14th January, 2026
Location
| Weekday | Time | Venue | Online | ID | Password |
|---|---|---|---|---|---|
| Wednesday | 14:20 - 16:55 | A14-201 | ZOOM 07 | 559 700 6085 | BIMSA |
Prerequisite
- Basic programming skills (e.g. Python) - Introduction to finance or financial economics - Basic knowledge of statistics and probability
Syllabus
- Introduction to Machine Learning and Financial Knowledge
- Supervised Learning: Regression and Classification
- Unsupervised Learning: Clustering and Dimensionality Reduction
- Ensemble Methods: Random Forests, Boosting, and Bagging
- Introduction to Quantitative Trading
- Machine Learning for Stock Price Prediction
- Portfolio Optimization and Risk Management using ML
- Credit Risk Modeling: Logistic Regression and Scorecards
- Advanced Credit Assessment
- Fraud Detection: Outlier Detection and Imbalanced Learning
- Neural Networks and Deep Learning in Financial Applications
- Model Evaluation and Explainability in Finance
- Supervised Learning: Regression and Classification
- Unsupervised Learning: Clustering and Dimensionality Reduction
- Ensemble Methods: Random Forests, Boosting, and Bagging
- Introduction to Quantitative Trading
- Machine Learning for Stock Price Prediction
- Portfolio Optimization and Risk Management using ML
- Credit Risk Modeling: Logistic Regression and Scorecards
- Advanced Credit Assessment
- Fraud Detection: Outlier Detection and Imbalanced Learning
- Neural Networks and Deep Learning in Financial Applications
- Model Evaluation and Explainability in Finance
Reference
- H. Markowitz, Portfolio Selection, The Journal of Finance 7 (1952) 77–91.
- E.F. Fama, K.R. French, Common risk factors in the returns on stocks and bonds, Journal of Financial Economics 33 (1993) 3–56.
- E.I. Altman, Financial Ratios, Discriminant Analysis and the Prediction of Corporate Bankruptcy, J Finance 23 (1968) 589–609.
- 石川, 刘洋溢, 连祥斌, 因子投资：方法与实践, 电子工业出版社, 北京, 2020.
- 周志华, 机器学习, 清华大学出版社, 2017.
- E.F. Fama, K.R. French, Common risk factors in the returns on stocks and bonds, Journal of Financial Economics 33 (1993) 3–56.
- E.I. Altman, Financial Ratios, Discriminant Analysis and the Prediction of Corporate Bankruptcy, J Finance 23 (1968) 589–609.
- 石川, 刘洋溢, 连祥斌, 因子投资：方法与实践, 电子工业出版社, 北京, 2020.
- 周志华, 机器学习, 清华大学出版社, 2017.
Audience
Advanced Undergraduate
, Graduate
Video Public
No
Notes Public
No
Language
Chinese
Lecturer Intro
Ruize Gao is Post-Doctoral Fellow at Beijing Institute of Mathematical Sciences and Applications and Tsinghua University. His research interests include digital economy, data economy and data mining. He has published in leading journals such as Information Sciences, Knowledge-based Systems, Expert Systems with Applications, Technology in Society, International Journal of Accounting Information Systems. He hosts one funding project under the China Postdoctoral Science Foundation.