PCA and BBP Phase Transition

Related lectures (28)

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Covers the application of Principal Component Analysis in facial recognition using a famous faces dataset.

Covers Gaussian Mixture Models, Denoising, Data Classification, and Spike Sorting using Principal Component Analysis.

Covers the process of diagonalizing matrices, focusing on symmetric matrices and the spectral theorem.

Covers the application of power calculation in matrices, focusing on reducing the matrix to calculate powers efficiently.

Explores eigenvalue problems, iterative methods, and their applications in quantum physics and network analysis.

Explores the generation of stochastic processes, including Gaussian processes, Markov processes, Poisson processes, and circulant embedding.

Explores linear regression in high dimensions and practical house price prediction from a dataset.

Explores the properties and examples of diagonalizable matrices, emphasizing the relationship between eigenvectors and eigenvalues.

Explores symmetric matrices, their diagonalization, and properties like eigenvalues and eigenvectors.

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