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

Computer Science Program.

Objectives

• 1. Identify basic IR models.
  2. Understand basic tokenizing, indexing, and implementation of Vector-Space Retrieval.
  3. Use query operations and languages.
  4. Apply experimental evaluation of IR.
  5. Differentiate categorization algorithms: Rocchio, nearest neighbor, and naive Bayes.
  6. Use naive Bayes text classification for ad hoc retrieval.
  7. Identify clustering algorithms: agglomerative clustering k-means expectation maximization (EM).
  8. Learn information extraction and integration.