EECS 476
Data Mining
Fundamental concepts and methods in data mining, and practical skills for mining massive, real data on distributed frameworks (e.g., Hadoop). Topics include big data systems, frequent itemsets, similarity and cluster analysis, classification, dimensionality reduction, mining of networks, time series and data streams, and applications (e.g., social network analysis, web search).
Topics
- big data systems
- frequent itemsets
- similarity and cluster analysis
- classification
- dimensionality reduction
- mining of networks
- time series and data streams
- and applications (e
Topics explicitly listed in official catalogue description
Prerequisites & course information
[EECS 281 and (MATH 214 or 217 or 296 or 417 or 419 or ROB 101); (C>, No OP/F)] or [EECS 403; (B or better, No OP/F)] or Graduate Standing in CSE. Enrollment in one minor elective allowed for Computer Science Minors. (Prerequisites enforced at registration.) (BS). May not be repeated for credit.
Sources
Catalogue variants
- Data Mining · 4 · College of Engineering catalogue
- Data Mining · Graduate credit