Foundations of Machine Learning with Applications in Python

June 29-July 3, 2026 in Amsterdam

Faculty

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

Janneke van Brummelen is an Assistant Professor at the Department of Econometrics and Data Science at the School of Business and Economics, Vrije Universiteit Amsterdam.

Meet the lecturers

Course

Research, policymaking, and business rely on ever-bigger data to answer wide-ranging questions. What are the risk factors for developing a disease? How to assess the risk profile of a new customer, when determining the appropriate insurance premium? How to best forecast unemployment? 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 foundational introduction to machine-learning techniques. Special attention is paid to the mathematical foundations of machine-learning algorithms, how to implement these from scratch, and how to avoid pitfalls such as overfitting.

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 other fields. We discuss how to implement machine-learning solutions in a broad sense; from conceptualizing the problem and implementing the appropriate techniques in Python, to evaluating the quality of your solution, overcoming challenges such as overfitting, and ensuring your solution is scalable and ready to be deployed.

Learning Objectives

After successfully completing this course, you 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 techniques, you will understand their mathematical and statistical foundations, and you will be able to reflect critically on their suitability for tackling the problem at hand. In addition, you will be able to implement simple machine-learning algorithms from scratch in Python, and you will be able to leverage existing machine-learning libraries such as scikit-learn and TensorFlow, to engineer more complex solutions.

Schedule

The course comprises a mix of lectures and computer lab sessions which are designed to foster an engaging and collaborative learning environment. In addition, the teachers will provide you with self-study instructions prior to and during the course. Such self-study is necessary for an effective learning process. The course spans five days, Monday to Friday.

Indicative timetable:

• 10:30 – 12:30: Lecture 1 and Lab Session 1
• 12:30 – 13:30: Lunch
• 13:30 – 16:30: Lecture 2 and Lab Session 2

Literature

We recommend the following books for background reading during the course:

• Hastie, Tibshirani, and Friedman (2009). The elements of statistical learning: data mining, inference, and prediction. 2nd edition. Springer. ISBN-13: 978-0387848570. Freely available at: https://hastie.su.domains/ElemStatLearn/printings/ESLII_print12_toc.pdf.download.html.
• Hastie, Tibshirani, and Wainwright (2015). Statistical learning with sparsity. 1st edition. Taylor & Francis. ISBN-13: 978-1498712163. Freely available at: https://hastie.su.domains/StatLearnSparsity_files/SLS.pdf.download.html.

In addition, the following books may serve as a useful reference:

• Provost and Fawcett (2013). Data science for business. 1st edition. O’Reilly. ISBN-13: 978-1449361327.
• James, Witten, Hastie, and Tibshirani (2013). An Introduction to Statistical Learning. Springer. 1st edition. ISBN-13: 978-1461471370. Available at: https://link.springer.com/content/pdf/10.1007/978-1-4614-7138-7.pdf.

• James, Witten, Hastie, Tibshirani, and Taylor (2023). An Introduction to Statistical Learning with Applications in Python. Springer. 1st edition. ISBN-13: 978-3031387463. Available at: https://hastie.su.domains/ISLP/ISLP_website.pdf.download.html

Level

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 specific machine-learning techniques. However, the course is experienced by some participants as challenging in terms of the relevant mathematics, statistics, and programming skills. Therefore, if you wonder if the course is suitable for you, the teachers encourage you take this self-test.

Admission requirements

Basic knowledge of Python and Jupyter Notebooks, and intermediate knowledge of matrix algebra, probability theory, and statistics.