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Tuesday, September 26, 2017Berkeley Distinguished Lectures in Data Science: On Computational Thinking, Inferential Thinking and Data ScienceSeminar | September 26 | 4:10-5 p.m. | 190 Doe Library Michael I. Jordan, Professor, Statistics & EECS, UC Berkeley Berkeley Institute for Data Science The rapid growth in the size and scope of datasets in science and technology has created a need for novel foundational perspectives on data analysis that blend the inferential and computational sciences. That classical perspectives from these fields are not adequate to address emerging problems in Data Science is apparent from their sharply divergent nature at an elementary level---in computer... More > Thursday, September 28, 2017GraphXD Seminar: Graph Clustering AlgorithmsSeminar | September 28 | 5:30-7 p.m. | 1011 Evans Hall Tselil Schramm, Simons Institute, UC Berkeley Berkeley Institute for Data Science One of the greatest advantages of representing data with graphs is access to generic algorithms for analytic tasks, such as clustering. In this talk I will describe some popular graph clustering algorithms, and explain why they are well-motivated from a theoretical perspective. Thursday, October 19, 2017GraphXD Seminar: Spectral Sparsification of GraphsSeminar | October 19 | 5:30-7 p.m. | 1011 Evans Hall Nikhil Srivastava, Dept. of Mathematics, UC Berkeley Berkeley Institute for Data Science Many important properties of an undirected graph manifest themselves spectrally in the eigenvalues or quadratic forms of matrices related to the graph. For instance, the connectivity structure, electrical properties, and random walk behavior of a graph are determined by its Laplacian matrix. A spectral sparsifier of a graph G is a sparse graph H on the same set of vertices such that the... More >
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