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Artificial Intelligence & Economics

Foundations of Machine Learning with Applications in Python

When:

18 August - 22 August 2025

School:

Tinbergen Institute & Business Data Science Summer School

Institution:

Tinbergen Institute & Business Data Science

City:

Amsterdam

Country:

Netherlands

Language:

English

Credits:

3.0 EC

Fee:

800 EUR

Interested?
Foundations of Machine Learning with Applications in Python

About

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 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

Ronald de Vlaming (Vrije Universiteit Amsterdam) and Janneke van Brummelen (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
fter 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 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.

Fee info

Fee

800 EUR, PhD and Master students (Early Bird Fee until April 15, 2025)

Fee

1500 EUR, Academics (incl. postdocs) & Professionals (Early Bird Fee until April 15, 2025)

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

Interested?

When:

18 August - 22 August 2025

School:

Tinbergen Institute & Business Data Science Summer School

Institution:

Tinbergen Institute & Business Data Science

Language:

English

Credits:

3.0 EC

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