Linear Algebra for Spectral Clustering

​This 60-minute webinar introduces the core linear algebra concepts behind spectral clustering, one of the most powerful methods for clustering in graphs and data analysis. We’ll explore eigenvalues, eigenvectors, and the graph Laplacian, focusing on their role in clustering algorithms.

​What you’ll learn:

• ​Clear introduction to the graph Laplacian and its properties

• ​How eigenvectors reveal hidden structures in data

• ​Worked examples and intuitive explanations

• ​Opportunity for Q&A at the end

​Important note:
This webinar is theory-focused. There will be no coding – the focus is on mathematical intuition and understanding.

​Audience:

​This webinar is suitable for advanced undergraduates, graduate students, and anyone interested in the mathematics of spectral clustering.