organized in cooperation with Helmholtz Information & Data Science Academy (HIDA) and Helmholtz AI
A Practical Guide to Dimensionality Reduction
This 4-hour course provides an introduction to the topic of dimensionality reduction and serves as a starting point for self-guided learning during and beyond the course time.
The course covers alternating sequences of theoretical input and hands-on exercises, which are discussed with the instructors during the course.
Dimensionality reduction is a common data preprocessing step preceding the application of supervised and unsupervised learning methods in AI modeling. After motivating the use of dimensionality reduction and highlighting its role in data exploration, this course gives an introduction to three types of dimensionality reduction approaches: feature transformation, feature aggregation, and feature selection. Course participants will have the opportunity to discover and compare the main methods for each approach in a hands-on experience, using jupyter notebooks on a real-world high-dimensional dataset.
Learning Goals
Part 1: General introduction and feature transformation methods
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General introduction to dimensionality reduction
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Theory and practical application of classical feature transformation methods
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Theory and practical application of autoencoders for feature transformation
Part 2: Further unsupervised and supervised methods
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Theory and practical application of feature aggregation approaches
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Theory and practical application of feature selection methods
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Stability optimization in feature selection
Prerequisites
Basic knowledge of Python. Basic understanding of machine learning models. Google account is recommended.
Target Group
This course is open to researchers of all career stages, or anyone interested in learning about the subject.
Course Days & Times
May 5, 2025, 10 am - 3 pm (lunch break 12 - 1 pm)
NOTE: Registration will open April 7, 2025, 12 pm.
Attendance & Certificates
The course content is coordinated, so we strongly recommend that you do not miss any part of the course. To receive a certificate we expect at least 80% attendance and active participation.
Registration & Cancellation
This course is open to individuals affiliated with Helmholtz or a HIDA Partner only.
Your registration for this course is binding. If you need to leave/miss the course for a period of time, please let us know in advance via hida-courses@helmholtz.de.
If you have to cancel the course for any reason, please do so as soon as possible to allow time for others to take your seat. To cancel, please withdraw your registration on the course site or write an email to hida-courses@helmholtz.de.
Additional Information
There is no waiting list for this course! If someone withdraws from a course, their place is automatically reopened. We therefore advise you to keep an eye on the registration in case the course is fully booked and you would like to attend. Also, this course will be offered again in the future - you can check our HIDA course catalog for updates.
This course is free of charge.
