General Information

The eScience Center offers a range of workshops and training courses, aimed at PhD candidates and other researchers or research software engineers. We organize workshops covering digital skills needed to put reproducible research into practice. These include online collaboration, reproducible code and good programming practices. We also offer more advanced workshops such as GPU Programming, Parallel Programming, Image Processing and Deep Learning.

This hands-on workshop will provide you with the basics of machine learning using Python.

Machine learning is the field devoted to methods and algorithms that ‘learn’ from data. It can be applied to a vast range of different domains, from linguistics to physics and from medical imaging to history.

This workshop covers the basics of machine learning in a practical and hands-on manner, so that upon completion, you will be able to train your first machine learning models and understand what next steps to take to improve them.

We start with data exploration and prepare the data so that it is suitable for machine learning. Then we learn how to train a model on the data using scikit-learn. We learn how to select the best model from the trained models and how to use different machine learning models (like linear regression, logistic regression, and decision tree models). Finally, we discuss some of the best practices when starting your own machine learning project.

Where: Polak Building - room 3.09 - Rotterdam. Get directions with OpenStreetMap or Google Maps.

When: juni 18 - 19, 2024, 9:00 - 17:00 CEST.

Requirements: Participants must bring a laptop with a Mac, Linux, or Windows operating system (not a tablet, Chromebook, etc.) that they have administrative privileges on. They should have a few specific software packages installed (listed below).

Contact: Please email or training@esciencecenter.nl for more information.

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Code of Conduct

Participants are expected to follow these guidelines:

• Use welcoming and inclusive language
• Be respectful of different viewpoints and experiences
• Gracefully accept constructive criticism
• Focus on what is best for the community
• Show courtesy and respect towards other community members

Syllabus

Machine learning concepts

• What is machine learning?
• Different types of machine learning
• Big picture of machine learning models
• General pipeline

The predictive modeling pipeline

• Tabular data exploration
• Fitting a scikit-learn model on numerical data
• Handling categorical data

Machine learning algorithms

• Intuitions on linear models
• Overview of machine learning algorithms

Machine learning best practices

• Data hygiene
• Correct evaluation
• How to keep your machine learning project organised

Applying machine learning on LISS dataset:

• How to prepare real-world data for machine learning?
• Take the first steps in predicting fertility using LISS data

Schedule

All times in the schedule are in the CEST timezone.

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Setup

To participate in this workshop, you will need access to software as described below. In addition, you will need an up-to-date web browser.

We maintain a list of common issues that occur during installation as a reference for instructors that may be useful on the Configuration Problems and Solutions wiki page.

Software setup

It is important that you setup everything on your laptop before the start of the course. This includes installing a Python environment and downloading the necessary files. Please follow these setup instructions. Send us an email if you encounter any problems.