Amsterdam, Netherlands

Foundations of Data Analysis and Machine Learning in Python

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, 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. During the lectures, we will introduce you to a wide variety of machine-learning techniques, ranging from linear and nonlinear regression models to dimensionality-reduction techniques and clustering methods, as well as deep learning using neural networks. During the lab sessions, we will guide you step by step through real-life case studies in economics, business, and medicine. 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.

Course leader

Lukas Hoesch is an Assistant Professor at the Department of Econometrics and Data Science at Vrije Universiteit Amsterdam. Ronald de Vlaming is an Assistant Professor at the Department of Economics at Vrije Universiteit Amsterdam.

Target group

The summer course welcomes (research) master students, PhD students and post-docs with a quantitative background and who are interested in understanding and applying state-of-the-art machine-learning techniques for classification, prediction, and forecasting. We also welcome professionals from policy institutions such as central banks or international firms and institutions. You do not need to have prior experience working with machine-learning techniques. However, the course will move at a fast pace. Therefore, prior exposure to implementing statistical models such as linear regression and maximum-likelihood estimation will make it considerably easier to follow the course.

Course aim

Learning Goals
After successfully completing this course, you will have the knowledge required to start solving problems in your own discipline using a wide range of machine-learning techniques. You will be able to communicate the core idea and intuition behind these algorithms with reference to their statistical foundations and reflect critically on their suitability for tackling the problem at hand. In addition, you will be comfortable implementing simple machine-learning algorithms from scratch in Python, as well as leveraging existing machine-learning libraries such as scikit-learn and TensorFlow to engineer more complex solutions.

Credits info

3 EC
Participants who joined at least 80% of all sessions will receive a certificate of participation stating that the summer school is equivalent to a workload of 3 ECTS. Note that it is the student’s own responsibility to get these credits registered at their university.

Fee info

EUR 800: Early Bird for PhD and Master students (available until April 15) € 800
PhD and Master Students € 1.000

The course fee covers tuition, course materials, daily lunches and coffee/tea during short breaks, social events including a dinner and farewell drinks. The course fee does not include accommodation.
EUR 2000: Academics (incl. postdocs) and Professionals € 2.000

The course fee covers tuition, course materials, daily lunches and coffee/tea during short breaks, social events including a dinner and farewell drinks. The course fee does not include accommodation.