Lectures related to Unsupervised Learning: Movie Recommendation

Covers Principal Component Analysis for dimension reduction in biological data, focusing on visualization and pattern identification.

Explains the construction of U, verification of results, and interpretation of SVD in matrix decomposition.

Explores Principal Component Analysis for dimensionality reduction and unsupervised feature selection.

Explores Singular Value Decomposition, low-rank approximation, fundamental subspaces, and matrix norms.

Explores Latent Semantic Indexing, a technique for mapping documents into a concept space for retrieval and classification.

Explores PCA and t-SNE for reducing dimensions and visualizing high-dimensional data effectively.

Covers the concept of Singular Value Decomposition (SVD) for compressing information in matrices and images.