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CPE
421
Information Retrieval and Organization
Introductory course for seniors and graduate students covering the practices, issues, and theoretical foundations of organizing and analyzing information and information content for the purpose of providing access to textual and non-textual information resources. Introduces students to the principles of unstructured information storage and retrieval systems.
Prerequisites:
0600304,0612207
0612421
(3-0-3)

Credits and Contact Hours

3 credits, 43 hours

Course Instructor Name

Dr. Mohammad Allaho

Textbook

Christopher D. Manning, Prabhakar Raghavan and Hinrich Schütze, "Introduction to Information Retrieval", Cambridge University Press. 2008. http://nlp.stanford.edu/IR-book/information-retrieval-book.html

W. Bruce Croft, Donald Metzler, and Trevor Strohman, "Search Engines, Information Retrieval in Practice". 2015

Catalog Description

This is an undergraduate-level introductory course for information retrieval. It will cover algorithms, design, and implementation of modern information retrieval systems. Topics include: retrieval system design and implementation, text analysis techniques, retrieval models (e.g., Boolean, vector space, probabilistic, and learning-based methods), search evaluation, retrieval feedback, search log mining, and applications in web information management.

Prerequisite

CpE-207 and ENGR-304

Specific Goals for the Course

Upon successful completion of this course, students will be able to:

  • Write code for text indexing and retrieval. (Student outcomes: 1, 6)
  • Evaluate information retrieval systems. (Student outcomes: 2)
  • Analyze textual and semi-structured data sets. (Student outcomes: 1, 6)
  • Evaluate information retrieval systems. (Student outcomes: 1, 6)
  • Learn about text similarity measures. (Student outcomes: 6)
  • Build specified search engines. (Student outcomes: 1, 4, 6)

Topics to Be Covered

  • Overview of text retrieval systems
  • Retrieval models and implementation: Vector Space Models
  • Query expansion and feedback
  • Probabilistic models; statistical language models
  • Text classification & Text clustering
  • Web search basics, crawling, indexes, Link analysis
  • Specialty search engine
  • IR applications