CSci 8363 -- Fall 2019

Daniel Boley

MW 4-5:15pm, Room Keller 3-115

We examine many methods for the exploration and analysis of very large data collections and how linear algebra has played a central role. Most of the class will be devoted to recent developments in the areas of • information retrieval, • data mining, • unsupervised clustering, • bioinformatics, • social networking, • machine learning. Examples of methods we will examine are • Latent Semantic Indexing, • Least Squares Fit, possibly under a sparsity constraint, • Spectral graph analysis, including graph Fourier transform and its use in neural networks, • Pagerank and other graph centrality measures, • Support Vector Machines, and • recent ideas on sparse approximation methods using L1 regularization. The class will be be based on recent papers published in these areas. Examples will be taken from vision recognition systems, biological gene analysis, document retrieval among others.


Students should be familiar with basic linear algebra concepts and methods such as Gaussian elimination for systems of linear equations, plus some familarity with. concepts such as matrix eigenvalues, singular values, and matrix least squares problems, though some time will be spent reviewing these latter topics. Basic concepts in optimization like first order optimality conditions and duality will also be useful.

Work Plan

Students will be expected to do the following. Your project will count toward the Project Requirements for a Plan C MS degree in Computer Science.

Sample Topics

For Further Information