11-641 Machine Learning for Text and Graph-based Mining
12 units, Carnegie Mellon University
This course studies the theory, design, and implementation of textbased information systems. The Information Retrieval core components of the course include statistical characteristics of text, representation of information needs and documents, several important retrieval models Boolean, vector space, probabilistic, inference net, language modeling, clustering algorithms, automatic text categorization, and experimental evaluation. The software architecture components include design and implementation of highcapacity text retrieval and text filtering systems. A variety of current research topics are also covered, including crosslingual retrieval, document summarization, machine learning, topic detection and tracking, and multimedia retrieval. Prerequisites Programming and datastructures at the level of 15212 or higher. Algorithms comparable to the undergraduate CS algorithms course 15451 or higher. Basic linear algebra 21241 or 21341. Basic statistics 36202 or higher.
Prerequisites: none listed
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