e-learning
Supervised Learning with Hyperdimensional Computing
Abstract
chopin2 implements a domain-agnostic supervised classification method based on the hyperdimensional (HD) computing paradigm. It is an open-source tool and its code is available on GitHub at https://github.com/cumbof/chopin2.
About This Material
This is a Hands-on Tutorial from the GTN which is usable either for individual self-study, or as a teaching material in a classroom.
Questions this will address
- How to encode data into vectors in a high-dimensional space?
- What kind of operations can be performed on these vectors?
- What is a vector-symbolic architecture?
- How to build a classification model out of this architecture?
Learning Objectives
- Learn how to encode data into high-dimensional vectors
- Build a vector-symbolic architecture
- Use the architecture to build a classification model
Licence: Creative Commons Attribution 4.0 International
Keywords: Statistics and machine learning
Target audience: Students
Resource type: e-learning
Version: 2
Status: Active
Prerequisites:
Introduction to Galaxy Analyses
Learning objectives:
- Learn how to encode data into high-dimensional vectors
- Build a vector-symbolic architecture
- Use the architecture to build a classification model
Date modified: 2023-11-09
Date published: 2023-04-28
Contributors: Fabio Cumbo
Scientific topics: Statistics and probability
External resources:
Activity log
