Summer course: Foundations of Machine Learning with Applications in Python

Research, policymaking, and business are increasingly reliant on the ability to retrieve and analyse ever-bigger data. What are the risk factors for developing a disease? Which individuals do we need to charge a higher insurance premium? How to best forecast inflation? How to optimally target online advertisements? Machine learning techniques are well-suited to answer such data-driven questions.

In this course, we provide a fast-paced and solution-oriented introduction to machine-learning algorithms. Special attention is paid to the theoretical foundations of machine-learning algorithms, as well as real-life applications. We discuss how to implement machine-learning solutions, from conceptualizing the problem and implementing the appropriate techniques in Python, to evaluating the quality of your solution and ensuring its scalability, as well as overcoming challenges such as overfitting.