| Dates |
Program Length |
Location |
| August 5th and 12th |
2 Weekends |
Hybrid: in-person and online |
Earn a professional certificate in Machine Learning for Investment and Trading
A solid grasp of the fundamentals of finance-applied machine learning is essential for career advancement. Gain the knowledge to address common finance challenges, such as financial forecasting and portfolio optimization, and drive positive change in your organization.
Learn these in-demand skills
- Gain a comprehensive understanding of supervised and unsupervised learning with an introduction to machine learning.
- Learn the fundamentals of Python and how to use it for data analysis in finance.
- Advance your knowledge with hands-on experience in classification algorithms.
- Develop skills in model assessment and selection to make better financial decisions.
- Discover the importance of microstructure, technical analysis, and algorithmic trading in the finance industry.
- Understand feature importance, model calibration, and backtesting to ensure the accuracy of your financial models.
- Optimize your portfolio and learn about factor models to achieve maximum returns.
- Explore the world of deep learning and its applications in financial forecasting.
Taught by a leading scholar in machine learning and algorithmic trading
Germán G. Creamer is an Associate Professor of quantitative finance and business analytics at Stevens Institute of Technology and an adjunct associate professor at Columbia University. Dr. Creamer has been a senior manager in the Risk, Information, and Banking Division for American Express, as well as an economic advisor to the president of Ecuador and the government of Equatorial Guinea. He has also consulted for several hedge funds and international organizations, including the United Nations, World Bank, and US Agency for International Development. He earned a Ph.D. in Computer Science (specialized in computational finance) and an MSc in Financial Engineering from Columbia University, and a Ph.D. in Economics from the University of Notre Dame.
