Compressed sensing is a signal processing technique that allows for efficient acquisition and reconstruction of signals by finding solutions to underdetermined linear systems. It exploits the sparsity of a signal to recover it from fewer samples than required by the Nyquist–Shannon sampling theorem. Recovery is possible when two conditions are met: sparsity and incoherence.
UC Berkeley
Fall 2013
This course investigates the mathematical principles behind data and information analysis. It brings together concepts from statistics, optimization, and computer science, with a focus on large deviation inequalities, and convex analysis. It's tailored towards advanced graduate students who wish to incorporate these theories into their research.
No concepts data
+ 20 more conceptsStanford University
Spring 2022
CS 168 provides a comprehensive introduction to modern algorithm concepts, covering hashing, dimension reduction, programming, gradient descent, and regression. It emphasizes both theoretical understanding and practical application, with each topic complemented by a mini-project. It's suitable for those who have taken CS107 and CS161.
No concepts data
+ 57 more conceptsStanford University
Fall 2022
This course dives into the use of randomness in algorithms and data structures, emphasizing the theoretical foundations of probabilistic analysis. Topics range from tail bounds, Markov chains, to randomized algorithms. The concepts are applied to machine learning, networking, and systems. Prerequisites indicate intermediate-level understanding required.
No concepts data
+ 37 more concepts