CWI highly values education. CWI researchers not only supervise and inspire the CWI PhD students, but also aim at contributing to teaching Master's students at universities. See the overview of CWI's current involvement in teaching courses in the Netherlands below.
CWI is a research institute and does not have education as an institutional task. However, many of the senior researchers of CWI contribute to education via an affiliation or part-time appointment at one of the Dutch universities.
The courses they teach are listed below in chronological order, starting with the earliest upcoming course.
In this course we focus on the fundamental ideas, theoretical frameworks, and rich array of mathematical tools and techniques that power machine learning.
Computational models are increasingly useful in modern neuroscience. In this course, we will cover advanced methods for analysis and simulation of neural systems looking at both biophysical and cognitive perspectives. We will focus on models of neural dynamics (using dynamical systems and biophysical models), perception and cognition (using artificial neural networks and brain-inspired deep learning approaches). The course will also contain a strong component of practical ‘hands-on’ work by the students – which will implement computational models during the course.
The course addresses which cognitive tasks can be executed by elementary networks, such as pattern recognition, classification of inputs in different categories and reward-dependent learning. This subject will be coupled to a discussion about the brain's capability to represent information in different modes, associated to either conscious or non-conscious processing.
The course is an introduction to modern statistical methods that are important for data science. An introduction course to statistics covering basic statistical concepts is a prerequisite.
During the course, several statistical approaches are introduced and discussed. A part of the lecture is devoted to the mathematical analysis of these methods. The course contains biweekly homework sheets that cover theoretical and practical problems.
This course is an introduction to causal inference. We will study formalisms to express causal relations, frameworks for reasoning about interventions and counterfactuals, and methods for discovering causal relations from data.
In this course we focus on the fundamental ideas, theoretical frameworks, and rich array of mathematical tools and techniques that power machine learning.
In this course, we will cover advanced methods for analysis and simulation of neural systems looking at both biophysical and cognitive perspectives.
