
Foundations of Machine Learning with Applications in Python
Short or summer 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 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
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Visit course websiteLanguage
English
Title
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Duration
5 days
ECTS credits
ECTS
The European Credit Transfer and Accumulation System (ECTS) is a student-centred system based on the student workload required to achieve the objectives of a programme of study. Its aim is to facilitate the recognition of study periods undertaken by mobile students through the transfer of credits. The ECTS is based on the principle that 60 credits are equivalent to the workload of full-time student during one academic year.
Accreditation
Information not available
Tuition fee 2025/2026
EU/EEA
The EU/EEA rate is the regular fee for students from within the EU/EEA.
€ 1,000
Admission
Application requirements
Basic knowledge of Python and Jupyter Notebooks, and intermediate knowledge of matrix algebra and statistics.
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.
Check when you can start and what you have to pay!
Tuition fees | |
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€ 1,000 | |
Non-EU/EEAThe non-EU/EEA rate is the rate for students from outside the EU/EEA. |
Information not available |
InstitutionalThe institutional rate is for all students who have already obtained a bachelor’s or master’s degree and who want to start a second programme leading to a degree at the same level or at a lower level. |
Information not available |
Start date | App. deadline EU/EEA | App. deadline Non-EU/EEA |
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18 Aug '25 | 3 Aug '25 | 3 Aug '25 |
Contact
Main addressDe Boelelaan 1105
1081 HV Amsterdam
020-5985020
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