Teaching

At the University of Amsterdam, I (co-)teach several courses related to neuroimaging, (Python) programming, and statistics and methodology, including machine learning and AI. I have made some of the material I developed for these courses publicly accessible, as described below.

NI-edu

Together with Noor Seijdel and Steven Scholte, I (re)designed and developed two courses about functional MRI analysis: Neuroimaging: BOLD-MRI and the follow-up course Neuroimaging: pattern analysis. Under the name NI-edu, I released the hands-on programming tutorials from these courses on the following website: https://lukas-snoek.com/NI-edu.

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introPy

The Research Master Psychology at the University of Amsterdam offers a full-time, four-week introductory programming course (“Programming in Psychological Science”). This course includes a two-week introduction to R followed by two weeks of advanced R or a two-week introduction to Python and the stimulus presentation program PsychoPy, which I developed. Like the fMRI courses, I made all the material publicly accessible on the following website: https://lukas-snoek.com/introPy.

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The Psychology of AI

This eight-week course features a new topic related to psychology and artificial intelligence, ranging from “good old fashioned AI” to deep learning to AI ethics. For this course, I created two introductory tutorials about neural networks for which students do not need any programming experience. Check them out below!

Building neural networks in your browser

This tutorial uses the online Tensorflow Playground app to introduce students to the idea of simple (“fully-connected”) neural networks. View it here!

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An introduction to convolutional neural networks

This tutorial focuses on convolutional neural networks (CNNs) and leverages the excellent CNN explainer web app to explain the key concepts behind CNNs. View it here!

screenshot_CNN_explainer

Creating your own CNN

For one of the group assignments of this course, students have to create, train, and evaluate their own CNN model on a topic/dataset of their choice using Teachable Machine. Check out the tutorial/assignment here!

screenshot_teachable_machine