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1 - 2 of 2 results for: CS145

CS 145: Data Management and Data Systems

Introduction to the use, design, and implementation of database and data-intensive systems, including data models; schema design; data storage; query processing, query optimization, and cost estimation; concurrency control, transactions, and failure recovery; distributed and parallel execution; semi-structured databases; and data system support for advanced analytics and machine learning. Prerequisites: 103 and 107 (or equivalent).
Terms: Aut | Units: 3-4 | UG Reqs: GER:DB-EngrAppSci

CS 246: Mining Massive Data Sets

Availability of massive datasets is revolutionizing science and industry. This course discusses data mining and machine learning algorithms for analyzing very large amounts of data. Topics include: Big data systems (Hadoop, Spark); Link Analysis (PageRank, spam detection); Similarity search (locality-sensitive hashing, shingling, minhashing, random hyperplanes); Stream data processing; Analysis of social-network graphs; Association rules; Dimensionality reduction (UV, SVD, and CUR decompositions); Algorithms for very-large-scale mining (clustering, nearest-neighbor search); Large-scale machine learning (gradient descent, decision tree ensembles); Multi-armed bandit; Computational advertising. We also offer a sister class CS246H (Hadoop Labs) and a follow-up project-based class CS341 (Project in Mining Massive Datasets). Prerequisites: At least one of CS107 or CS145.
Terms: Win | Units: 3-4
Instructors: Leskovec, J. (PI)
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