Mining Massive Data Sets
Lecture
Main Campus
Course Description
The 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, min-hashing); Stream data processing; Recommender Systems; Analysis of social-network graphs; Association rules; Dimensionality reduction (UV, SVD, and CUR decompositions); Algorithms for large-scale mining (clustering, nearest-neighbor search); Large-scale machine learning (decision tree ensembles); Multi-armed bandit; Computational advertising. Prerequisites: At least one of CS107 or CS145.
CS 246
Units: 3–4
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Typically Offered
Not Available
Grading Basis
Letter or Credit/No Credit
Class Level
Undergraduate
Instructional Mode
In Person
Gen Ed Requirement(s)
WAYS - Formal Reasoning (FR)
