Compressed Sensing Courses and Certifications

Steve Brunton

Compressed Sensing and Dynamic Mode Decomposition

Explore leveraging compressed sensing for dynamic mode decomposition, enabling analysis of under-sampled data. Learn techniques to extract meaningful insights from limited information.

• YouTube
• 31 minutes
• On-Demand
• Free Video

Alan Turing Institute

On the Unreasonable Effectiveness of Compressive Imaging - Ben Adcock, Simon Fraser University

Explores compressive imaging's effectiveness, discussing compressed sensing theory, optimal sampling strategies, and practical applications in data science and imaging technology.

• YouTube
• 41 minutes
• On-Demand
• Free Video

Fields Institute

A Family of Measurement Matrices for Generalized Compressed Sensing

Explore generalized compressed sensing with a family of measurement matrices, focusing on signal recovery from noisy linear measurements and the role of effective rank in guaranteeing accurate recovery.

• YouTube
• 44 minutes
• On-Demand
• Free Video

USC Probability and Statistics Seminar

Geometric Constructions for Sparse Integer Signal Recovery

Explore geometric constructions for sparse integer signal recovery, focusing on matrix design for robust vector reconstruction in compressed sensing applications.

• YouTube
• 47 minutes
• On-Demand
• Free Video

Broad Institute

Sparsity, Epistasis, and Models of Fitness Functions - Leveraging Interactions in Protein Function Prediction

Explore sparsity in epistatic interactions to enhance fitness function modeling. Learn about Walsh-Hadamard transform, Graph Fourier transforms, and Epistatic Net for improved protein function prediction.

• YouTube
• 1 hour 37 minutes
• On-Demand
• Free Video

Computational Memory - A Stepping-Stone to Non-Von Neumann Computing?

Explore computational memory as a potential alternative to traditional computing, covering in-memory computing, resistive memory devices, and applications in AI and data processing.

• YouTube
• 1 hour 21 minutes
• On-Demand
• Free Online Course

University of Tübingen

Statistical Machine Learning

2 reviews

Comprehensive exploration of machine learning concepts, from basic algorithms to advanced topics like kernels, dimensionality reduction, and clustering, with insights on societal impact and practical applications.

• YouTube
• 1 day 9 hours
• On-Demand
• Free Online Course

Steve Brunton

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.

• YouTube
• 19 hours
• On-Demand
• Free Video

Steve Brunton

Koopman Analysis

Explore advanced techniques in dynamical systems analysis, including Koopman theory, spectral analysis, and applications to control and chaos in various scientific domains.

• YouTube
• 3 hours 30 minutes
• On-Demand
• Free Video

Steve Brunton

Dynamical Systems

Explore advanced techniques in dynamical systems analysis, including SINDy, Koopman theory, and deep learning applications for modeling complex nonlinear systems.

• YouTube
• 6 hours
• On-Demand
• Free Video

Steve Brunton

Sparsity and Compression

Explore sparsity and compression techniques, from compressed sensing to robust regression, with practical applications in image processing, signal analysis, and machine learning.

• YouTube
• 4 hours 30 minutes
• On-Demand
• Free Video

Steve Brunton

Intro to Data Science

Comprehensive introduction to data science and machine learning, covering key concepts, techniques, and applications. Includes hands-on examples in MATLAB and Python for practical implementation.

• YouTube
• 22 hours
• On-Demand
• Free Video

Hausdorff Center for Mathematics

Helmut Bölcskei - Lossy Compression on General Data Structures

Explore lossy compression for data on manifolds and fractals, with bounds on rate-distortion function and quantization error. Applications in data science and image processing.

• YouTube
• 44 minutes
• On-Demand
• Free Video

Hausdorff Center for Mathematics

Solving Inverse Problems With Deep Neural Networks - Robustness Included?

Exploring robustness of deep learning in inverse problems, focusing on resilience against statistical noise and adversarial perturbations in image reconstruction tasks.

• YouTube
• 29 minutes
• On-Demand
• Free Video

Society for Industrial and Applied Mathematics

Signal Recovery with Generative Priors - SIAM-IS Virtual Seminar

Explore signal recovery using generative priors in imaging, discussing neural network models, recovery guarantees, and applications in compressed sensing and phase retrieval.

• YouTube
• 1 hour 3 minutes
• On-Demand
• Free Video