Singular Value Decomposition
Comprehensive exploration of Singular Value Decomposition, covering mathematical foundations, applications in data processing, and implementation in MATLAB and Python for various real-world scenarios.
Data-Driven Science and Engineering
Explora técnicas avanzadas de aprendizaje automático, sistemas dinámicos y control con ejemplos prácticos en Matlab y Python, basados en el libro "Data-Driven Science and Engineering" de Brunton y Kutz.
Linear System Theory
We will cover time invariant, time varying, continuous and discrete time systems. This course will cover concepts of stability, controllability, observability, and design and serve as a necessary foundation for further study in the area of systems and co…
Applied Linear Algebra
Explore vector spaces, linear maps, eigenvalues, and applications in systems, algorithms, and optimization. Gain practical skills in linear algebra for various fields.
Linear Algebra Basics for Computer Vision - Lecture 2
Explore essential linear algebra concepts including vectors, matrices, transformations, and SVD, with practical applications in computer vision and machine learning.
Matrices
Explore advanced matrix concepts including Kronecker product, spectral decomposition, singular value decomposition, and projection matrices, with applications in statistics and linear algebra.
Unsupervised Learning
Explore key unsupervised learning techniques including clustering, dimensionality reduction, and generative models. Gain insights into real-world applications like recommendation systems and image compression.
Advanced Matrix Theory and Linear Algebra for Engineers
Comprehensive exploration of advanced linear algebra concepts for engineers, covering vector spaces, transformations, orthogonality, diagonalization, and singular value decomposition.
Linear Algebra
Comprehensive exploration of linear algebra concepts, from basic equations to advanced topics like eigenvalues and singular value decomposition, with practical applications and proofs.
Sparse Nonlinear Dynamics Models with SINDy - The Library of Candidate Nonlinearities
Explores advanced techniques for SINDy algorithm, including control variables, bifurcation parameters, rational functions, dimensionality reduction, and symmetry constraints in nonlinear dynamics modeling.
Linear Algebra
Comprehensive exploration of linear algebra concepts, including SVD, Fourier analysis, and compressed sensing, with practical applications and coding examples in MATLAB and Python.
Principal Component Analysis - Lecture 8
Explore Principal Component Analysis: from matrix rank to SVD. Learn to measure data cloud size, understand correlated data, and navigate high-dimensional spaces using statistical techniques.
Structure and Matrices in Julia Programming - Lecture 3
Explore matrices, sparse structures, and SVD for image compression and computational thinking in Julia, with practical applications in machine learning and linear algebra.
MVG Mechanism - Differential Privacy under Matrix-Valued Query
Explore a novel differential privacy mechanism for matrix-valued queries, addressing challenges in data protection and improving upon existing methods.
Foundations of Data Science II
Explore advanced data science concepts including SVD, Gaussian mixtures, and Markov chains. Learn about randomized algorithms and low-rank approximations for efficient data handling.
Intro and Foundations of Data Science I
Explore key concepts in data science, including random variables, distributions, dimension reduction, and singular value decomposition, with experts from Microsoft Research India.
Mathematical Representations and Models - Professor Jared Tanner, Oxford University
Explore mathematical models for high-dimensional data analysis with Oxford's Prof. Tanner. Learn about Fourier series, localisation, coherence, and advanced techniques for efficient information extraction and processing.
Singular Value Decomposition and Image Compression
Learn singular value decomposition (SVD) and its application in image compression. Explore matrix transformations, dimensionality reduction, and practical implementation techniques.
Learning-Based Low-Rank Approximations - IPAM at UCLA
Explores learning-based algorithms for low-rank matrix approximation, focusing on optimizing sketching matrices using training data to significantly reduce approximation errors in efficient computational methods.
SVD Part 2
Explore advanced concepts of Singular Value Decomposition (SVD) in data analysis and dimensionality reduction, building on fundamental principles to enhance understanding of matrix factorization techniques.
Singular Value Decomposition with R - Introduction to Text Analytics with R Part 8
Learn to perform truncated SVD, project TF-IDF vectors into semantic space, compare decision tree and random forest models, and explore random forest tuning using R packages like irlba and caret for text analytics.