Algorithmic Techniques For Handling Large, High-dimensional Datasets For Which We Can Only Afford Linear Or Even Sub-linear Time and Space Resources. Mathematical Foundations (large Deviation Bounds, Normed Spaces). Streaming, Lower Bounds For Streaming Using Communication Complexity, Dimensionality Reduction and The Johnson-lindenstrauss Lemma, Locality Sensitive Hashing (lsh), Large Scale Numerical Linear Algebra, The Matrix Completion Problem, Convex Relaxations Of Low Rank, The Mapreduce Distributed Computation Paradigm. Learning Outcomes# at The End of The Course, The Students Will Understand Important Mathematical Principles For Data Intensive Algorithms, and Know How to Use Them in Modern Algorithm Design.
Faculty: Computer Science
(94412 - Probability (advanced) and 104166 - Algebra Am and 104167 - Algebra A and 234218 - Data Structures 1) or (104034 - Introduction to Probability H and 104166 - Algebra Am and 104167 - Algebra A and 234218 - Data Structures 1)