Course content

Introduction to machine learning and AI methods with economic applications is an intensive PhD course offered through the project "COMPutational economics and optimization - Agents, Machines and Artificial intelligence" (COMPAMA). COMPAMA is developing an emerging interdisciplinary area in the borderland between economics, optimization, psychology, machine learning and AI with the main purpose to understand the economic impact of decisions, made by both machines and human agents.

This course will give an overview of machine learning methods within the AI framework. Economic applications for the learned methods will be presented and explored. The main goal is that students without previous knowledge in the area of machine learning and AI can understand and apply the models in their research topic. Examples of relevant applications are customer and marketing segmentation, credit risk assessment, forecasting and fraud detection.

Learning outcome

After having completed the course the candidate should be able to:

• explain and implement the different methods learned;
• choose the more suitable method for a specific economic application;
• recognize the opportunities and challenges of using AI in each context.

Learning methods and activities

Lectures. Participation in the seminars is expected, which includes attendance at all lectures, as well as contributions to the discussions. There will be compulsory activities in the course.

Compulsory assignments

• Participation and compulsory activities

Recommended previous knowledge

This course is designed for PhD candidates within the fields operations research, finance and economics. Programming skills are recommended.

Required previous knowledge

Admission to a PhD programme within operations research, or completed masters courses in optimization.

Course materials

Selected literature. Will be given at course start-up.