Course 2023-2024 a.y.

1. Introduce finance / banking theory and present main concepts
2. Illustrate how big data and AI can improve financial decision-making
3. Provide students with a foundation for performing data analytics in finance-related roles both inside the financial sector (e.g., commercial and investment banking, venture capital, private equity, asset management) and outside the financial sector (e.g., consulting, general management, corporate development).

This course is not intended to be a substitute for an econometrics course or for a machine learning course. Instead, this course is designed as a complement to these courses. Thus, the course assumes that students have prior exposure to statistics and data analysis.

• Master key theoretical concepts in finance, banking, and monetary economics
• Understand how big data and AI changed the way finance is practiced
• Test and select adequate machine learning strategies for financial applications
• Identify origins of bias in machine learning models for credit screening, firm failure
•  Understand key technological, strategic, and regulatory aspects of new FinTech business models

• Handle, clean and analyze structured and unstructured financial data
• Apply advanced ML techniques to answer real-world financial questions currently confronting finance professionals
• Choose adequate technologies, data sources and machine learning models to support a FinTech business idea or an academic research project

• Face-to-face lectures
• Exercises (exercises, database, software etc.)
• Individual assignments
• Group assignments

This course is designed for a high level of participation and interaction. We'll have face-to-face lectures, complemented by hands-on lab classes in which we develop prototypes of what was discussed in the lectures, run simulations, or let the models compete against each other. Furthermore, there are long-term project which give the students plenty of opportunity to develop and demonstrate your own ideas. Due to the high degree of in-class interactivity and extensive computer work, attendance is strongly recommended.

Your course grade is based on four components:

1. Several data labs account for 30% of the course grade. You will work in teams on the labs. Your lowest lab score will be dropped.
2. A final project or paper presentation accounts for 30% of the course grade
3. A final written exam accounts for 30% of the grade
4. Class participation, as measured by quality of course engagement, comprises the final 10% of the course grade

All relevant teaching materials are made available via Bboard. We use slack to communicate with all class participants; more detailed information about this will be available on the Bboard course page. The full list of academic articles and textbooks will be communicated in class.