Locality-sensitive hashing is an algorithmic technique used for data clustering and nearest neighbor search. It hashes similar input items into the same "buckets" while maximizing hash collisions, and can be seen as a way to reduce the dimensionality of high-dimensional data. It is one of two main categories of hashing methods, the other being locality-preserving hashing.
Stanford 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.
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