Lectures related to Low-rank approximation

Explores low rank approximations, spectral theorems, and orthogonality in matrices.

Explores Singular Value Decomposition and Principal Component Analysis for dimensionality reduction, with applications in visualization and efficiency.

Singular Value Decomposition: Theory and Applications

Explores Singular Value Decomposition theory, properties, uniqueness, matrix approximation, and dimensionality reduction applications.

Unsupervised learning: Young-Eckart-Mirsky theorem and intro to PCA

Introduces the Young-Eckart-Mirsky theorem and PCA for unsupervised learning and data visualization.

Unsupervised Learning: Principal Component Analysis

Covers unsupervised learning with a focus on Principal Component Analysis and the Singular Value Decomposition.

Transformer Architectures: Subquadratic Attention Mechanisms

Covers transformer architecture and subquadratic attention mechanisms, focusing on efficient approximations and their applications in machine learning.