Mathematics
Foundations of Data Science – Lecture 9 – Two Applications of SVD
Modern data often consists of feature vectors with a large number of features. High-dimensional geometry and Linear Algebra (Singular Value Decomposition) are two of the crucial areas which form the mathematical foundations of Data Science.…
Foundations of Data Science – Lecture 8 – Low Rank Approximation (LRA) via Length Squared Sampling
Modern data often consists of feature vectors with a large number of features. High-dimensional geometry and Linear Algebra (Singular Value Decomposition) are two of the crucial areas which form the mathematical foundations of Data Science.…
Foundations of Data Science – Lecture 7 – Singular Value Decomposition – ll
Modern data often consists of feature vectors with a large number of features. High-dimensional geometry and Linear Algebra (Singular Value Decomposition) are two of the crucial areas which form the mathematical foundations of Data Science.…
Foundations of Data Science – Lecture 6 – Singular Value Decomposition – l
Modern data often consists of feature vectors with a large number of features. High-dimensional geometry and Linear Algebra (Singular Value Decomposition) are two of the crucial areas which form the mathematical foundations of Data Science.…
Pacific Northwest Probability Seminar: A Characterization Theorem for the Gaussian Free Field
We prove that any random distribution satisfying conformal invariance and a form of domain Markov property and having a finite moment condition must be the Gaussian free field. We also present some open problems regarding…
