Matrix Decompositions Courses and Certifications

LoRKD - Low-Rank Knowledge Decomposition for Medical Foundation Models

Discover how to decompose medical foundation models into specialized lightweight experts using Low-Rank Knowledge Decomposition for enhanced task performance.

• YouTube
• 49 minutes
• Self-Paced
• Free Video

MITCBMM

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.

• YouTube
• 54 minutes
• Self-Paced
• Free Video

Joint Mathematics Meetings

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.

• YouTube
• 47 minutes
• Self-Paced
• Conference Talk

Gautam Varde

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.

• YouTube
• 11 minutes
• Self-Paced
• Free Video

Simons Institute

Analyzing Optimization and Generalization in Deep Learning via Dynamics of Gradient Descent

Explore deep learning optimization and generalization through gradient descent dynamics, focusing on implicit preconditioning and its impact on matrix completion problems.

• YouTube
• 44 minutes
• Self-Paced
• Free Video

Simons Institute

Quantum-Inspired Classical Linear Algebra

Explore quantum-inspired classical algorithms for linear algebra, including sketching, sampling, and low-rank approximation techniques in this advanced computational talk.

• YouTube
• 59 minutes
• Self-Paced
• Free Video

Simons Institute

Advanced Techniques for Low-Rank Matrix Approximation

Explore cutting-edge methods for low-rank matrix approximation, including randomized algorithms and factorizations, with insights on efficiency and accuracy comparisons.

• YouTube
• 32 minutes
• Self-Paced
• Free Video

Simons Institute

Sampling for Linear Algebra, Statistics, and Optimization I

Explore randomized numerical linear algebra techniques for efficient data analysis, covering sampling methods, matrix multiplication, and regression algorithms.

• YouTube
• 1 hour 3 minutes
• Self-Paced
• Free Video

Simons Institute

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.

• YouTube
• 55 minutes
• Self-Paced
• Free Video

Simons Institute

Finding Low-Rank Matrices - From Matrix Completion to Recent Trends

Explore low-rank matrix recovery, from matrix completion to recent trends, covering theoretical foundations and applications in signal processing and machine learning.

• YouTube
• 54 minutes
• Self-Paced
• Free Video

The Julia Programming Language

Interplay of Linear Algebra, Machine Learning, and HPC - JuliaCon 2021 Keynote

Explore the interplay of linear algebra, machine learning, and HPC with Dr. Li. Learn about fast solvers, low-rank compression, and how ML can optimize linear algebra computations for big data problems.

• YouTube
• 40 minutes
• Self-Paced
• Free Video

Institute for Pure & Applied Mathematics (IPAM)

Hyper-Optimized Tensor Network Contraction - Simplifications, Applications and Approximations

Explore tensor network contraction optimization through hyper-graph partitioning, simplifications, and approximations, with applications in quantum circuit simulation and beyond.

• YouTube
• 33 minutes
• Self-Paced
• Free Video

CppCon

Guide to Linear Algebra With the Eigen C++ Library

Explore the Eigen C++ library for linear algebra, covering setup, matrix decompositions, STL algorithm compatibility, and integration with C++23/26 features for scientific programming applications.

• YouTube
• 1 hour 1 minute
• Self-Paced
• Free Video

Society for Industrial and Applied Mathematics

Low Rank Tensor Completion and Its Applications

Explore low rank tensor completion methods using framelet, dictionary coding, deep plug-and-play prior, and more. Gain insights into theoretical and numerical results for solving complex imaging problems.

• YouTube
• 55 minutes
• Self-Paced
• Free Video

Analyse numérique pour ingénieurs

110 ratings at Coursera

Méthodes numériques pour ingénieurs : interpolation, dérivation, intégration, systèmes linéaires et non-linéaires, équations différentielles. Approche pratique avec MATLAB/Octave et exercices théoriques.

• Coursera
• 37 hours
• Self-Paced
• Paid Course