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CS 246: Mining Massive Data Sets

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.
Terms: Win | Units: 3-4 | UG Reqs: WAY-FR
Instructors: ; Leskovec, J. (PI); Baby, S. (TA); Bai, D. (TA); Catasta, M. (GP); Kang, D. (TA); Li, Y. (TA); Liu, C. (TA); Romain, M. (TA); Yau, J. (TA); Yim, T. (TA); Zhao, R. (TA); Zheng, A. (TA); Zhou, Y. (TA); Zhu, M. (TA)

CS 246H: Mining Massive Data Sets Hadoop Lab

Supplement to CS 246 providing additional material on the Apache Hadoop family of technologies. Students will learn how to implement data mining algorithms using Hadoop and Apache Spark, how to implement and debug complex data mining and data transformations, and how to use two of the most popular big data SQL tools. Topics: data mining, machine learning, data ingest, and data transformations using Hadoop, Spark, Apache Impala, Apache Hive, Apache Kafka, Apache Sqoop, Apache Flume, Apache Avro, and Apache Parquet. Prerequisite: CS 107 or equivalent.
Terms: Win | Units: 1
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