11-643 Machine Learning for Text Analysis
12 units, Carnegie Mellon University
(Previously Scalable Analytics)This is a full-semester course (12 units), intended for students in professional PhD/master programs and undergraduates who meet the pre-requisites. Replacing the 2nd half of 11-641/11-441 (Search Engines and Web Mining), this new course offers a blend of core theory, important applications, and implementation of scalable analytic techniques. Specifically, it covers high-dimensional data representation, dimensionality reduction, clustering, collaborative filtering, large scale classification, learning to rank, link analysis, statistical significance tests, and more. Homework assignments give hands-on experiences to students by implementing representative algorithms, conducting empirical evaluations, and exercising the main concepts taught in the course. Prerequisites ? Data structures & algorithms (e.g. 15-213) (required) ? Matrix or Linear Algebra (e.g. 21-241 or 21-341) (required) ? Basic Probability and Statistics (e.g. 21-325) (required) ? 15-451, Algorithm Design and Analysis (not required but helpful) ? 10-601 or 10-701, Machine Learning (not required but helpful) For CMU CS undergraduates, all of the required courses need to be completed before or during the junior year; for MS students, equivalent background is required.
Prerequisites: 15-213 and 21-325 and 21-241
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