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, representation of information needs and documents, several important retrieval models (boolean, vector space, probabilistic, inference net, and language modeling), clustering algorithms, collaborative filtering, automatic text categorization, and experimental evaluation. The software architecture components 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

Bachelor of Computer Science - 132 CRs

Objectives

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