Research, policymaking, and business rely on ever-bigger data to answer wide-ranging questions. 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, you will learn about a wide variety of machine learning techniques, ranging from linear and non-linear regression models to dimensionality-reduction techniques, clustering methods and deep learning using artificial neural networks. Special attention will be paid to both a theoretical understanding of the various methods as well as to real-life applications of the techniques using Python.