Explore advanced sketching techniques for kernel density estimation in this 46-minute lecture by Moses Charikar from Stanford University. Delve into the intersection of sketching and algorithm design, focusing on innovative approaches to handle large-scale data efficiently. Gain insights into cutting-edge methods for approximating kernel density functions, essential for various machine learning and data analysis applications. Understand how these techniques can significantly reduce computational complexity while maintaining accuracy in high-dimensional spaces.
Sketching Techniques for Kernel Density Estimation
Overview
Syllabus
Sketching Techniques for Kernel Density Estimation
Taught by
Simons Institute
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