Linear Algebra

Explore fundamental linear algebra concepts that underpin unsupervised machine learning algorithms. Dive into topics such as eigenvectors and eigenvalues in clustering, Laplacian matrices and graph partitioning, and applications of spectral decomposition. Gain a deeper theoretical understanding to enhance your data analysis and algorithm implementation skills.

Eigenvectors and Eigenvalues in Clustering

An in-depth exploration of the role of eigenvectors and eigenvalues in clustering algorithms, including their mathematical foundations and applications in spectral clustering.

Laplacian Matrices and Graph Partitioning

A deep dive into Laplacian matrices and their role in graph partitioning, with a focus on applications in spectral clustering and unsupervised learning.

Applications of Spectral Decomposition in Clustering

An in-depth exploration of how spectral decomposition is applied in clustering algorithms, including spectral clustering, community detection, and dimensionality reduction.