CSC 575
Winter 2017


 Course Material 


 Class Project 

 Online Resources 



Intelligent Information Retrieval

Course Syllabus


Bamshad Mobasher
Office: Loop Campus, CDM Building, Room 833
Phone: (312) 362-5174
Office Hours: Mon. 4:00-5:30 PM (or by appointment)

Textbooks and Reading Material:

  • Introduction to  Information Retrieval, by Christopher Manning, Prabhakar Raghavan and Hinrich Schutze, Cambridge University Press. 2009.  [Note: This book is available online from the above link; In the reading assignments it is referred to as the "IR Book"].
  • Research and Reference Articles (provided online).


CSC 403 (or equivalent background in data structures)

Grading Policy:

The final grade will be determined based on the following components:

Assignments = 65%
Final Project = 35%

The general grading scheme will be based on a curve, but the grade cutoffs will be no higher than: A = 90-100%, B = 80-89%, C = 65-79%, D = 50-64%, F = 0-49%. Within each grading range +/- grading will be used. At the end of the quarter, some adjustments may be made based on overall class performance as well as signs of individual effort.


There will be 4-5 assignments involving problems related to concepts and techniques discussed in class, as well as experiments with various tools or systems. Unless otherwise specified, these assignments must be done individually. Generally, late assignments will be penalized 10% per day (with weekends counting as one day). No late assignments will be accepted after the "latest submission date" (specified in class for each assignment). All assignments must be submitted electronically (see the Assignments Section for more detail).

Course Project

The final project for the class can be either an implementation project or a written project. The implementation projects would involve the design and implementation of an information retrieval, filtering, or integration system (or a specific part of a system). The written projects must involve a detailed study, survey, and evaluation of one or more topics or systems related to information retrieval and filtering. Written projects must be done individually, while implementation may be done individually or in groups of up to 3 people (depending the size and the complexity of the project). Each group or individual must submit a specific project proposal to be approved. A list of project ideas and some additional details regarding the projects and deadlines are available in the Project Section

Tentative List of Topics

  • Overview of IR Systems, Historical Perspectives, Basic Evaluation.
  • Document Representation: Statistical Characteristics of Text, Basic Query Processing.
  • Data Structure and File Organization for IR.
  • Automatic Indexing and Indexing Models.
  • Retrieval Models: Similarity Measures and Ranking, Boolean Matching, Vector Space Models, Probabilistic Models
  • Search and Filtering Techniques: Relevance Feedback, User Profiles, Collaborative Filtering
  • Document and Term Clustering, Document Categorization.
  • IR Systems and the WWW, PageRank and Hyperlink Analysis, Search Personalization
  • Web Mining and Its Applications

Copyright © 2016-2017, Bamshad Mobasher, DePaul University.