Matrices
Explore advanced matrix concepts including Kronecker product, spectral decomposition, singular value decomposition, and projection matrices, with applications in statistics and linear algebra.
Diagonalization of a Matrix - Numerical Examples in Linear Algebra
Learn to diagonalize matrices through clear examples, enhancing your understanding of linear algebra and its applications in various mathematical and scientific fields.
Introduction to Linear Algebra for Data Science
Explore core linear algebra concepts in data science, from vector representations to matrix factorization, with real-world applications in text similarity, image processing, and content recommendation.
Dimensionality Reduction for Matrix- and Tensor-Coded Data
Explore matrix and tensor factorizations for data compression, including PCA, NMF, and CP tensor decomposition. Learn theoretical foundations and practical applications in scientific domains.
Unsupervised Feature Learning with Matrix Decomposition - Aedin Culhane, PhD | ODSC East 2018
Explore unsupervised learning techniques for high-dimensional data analysis, focusing on matrix factorization methods and their applications in oncology and precision medicine.
Exploring Random Quantum Circuits: A New Implementation Method
Discover a novel method for constructing random quantum circuits, comparing its properties to traditional approaches and exploring its potential for quantum hardware implementation.
Tensor Decomposition - A Mathematical Tool for Data Analysis
Explore tensor decomposition as a powerful mathematical tool for analyzing complex data structures, with applications in neuroscience, gas detection, and communication patterns.
Simple Factorization and Other Properties of D-Dimensional Unitaries
Explore d-dimensional unitaries, their factorization properties, and average characteristics in quantum computing. Gain insights into constructing and analyzing higher-dimensional quantum transformations.
Singular Value Decomposition and Rank-K Approximation - Lecture 19
Dive into Singular Value Decomposition (SVD) concepts, exploring vector path tracing and rank-k approximation techniques for advanced matrix analysis and dimensionality reduction.