Information Retrieval

Description

This course studies the theory, design, and implementation of text-based information systems. The Information Retrieval core components of the course include statistical characteristics of text, repre-sentation of information needs and documents, several important retrieval models (boolean, vector space, probabilistic, inference net, and language modeling), clustering algorithms, collaborative fil-tering, automatic text categorization, and experimental evaluation. The software architecture compo-nents include design and implementation of high-capacity text retrieval and text filtering systems. It also introduces web search including crawling, link-based algorithms, and Web metadata; text/Web clustering, classification; text mining.

Program

Computer Science Program.

Objectives

• 1. Identify basic IR models.
  2. Understand basic tokenizing, indexing, and implementation of Vector-Space Retrieval.
  3. Apply experimental evaluation of IR.
  4. Differentiate categorization algorithms: SVM, Rocchio, nearest neighbor, and naive Bayes.
  5. Learn clustering algorithms: agglomerative clustering; k-means.
  6. Learn Information retrieval concepts in World Wide Web domain.