CS429: Information Retrieval
Illinois Institute of Technology
- See the Schedule for a detailed list of readings and due dates.
- See https://github.com/iit-cs429/assignments for assignments.
- Course: CS 429: Information Retrieval
- Instructor: Dr. Aron Culotta
- Meetings: 3:15 - 4:30 pm T/R Stuart 104
- E-mail: culotta at cs.iit.edu
- Phone: 312-567-5261
- Office Hours: T/R 10:00 a.m. - 11:00 a.m.
- Office: Stuart Hall 229B
- Zhao Wang; Office hour W 1-2 in Stuart 002
- Ehsan Ardehaly; Office Hours M 12-1 in Stuart 002
Description: Overview of fundamental issues of information retrieval with theoretical foundations. The information-retrieval techniques and theory, covering both effectiveness and run-time performance of information-retrieval systems are covered. The focus is on algorithms and heuristics used to find documents relevant to the user request and to find them fast. The course covers the architecture and components of the search engine such as parser, stemmer, index builder, and query processor. The students learn the material by building a prototype of such a search engine. Prerequisites: CS 331 or CS 401; requires strong programming knowledge. 3-0-3 (C) (T)
Textbook: Introduction to Information Retrieval, Christopher D. Manning, Prabhakar Raghavan and Hinrich Schütze, Cambridge University Press. 2008.
You can use the electronic version of this book.
- 250 points - Assignments (5 @ 50 points each)
- 100 points - Midterm
- 100 points - Final
- 60 points - Quizzes (4 @ 15 points each)
- 510 total points
- Please read IIT's Academic Honesty Policy
- All work you turn in must be done by you alone.
- All violations will be reported to
firstname.lastname@example.org may result in a failing grade for the assignment and/or course.
Late Submission Policy
- Late assignments will not be accepted, unless:
- There is an unavoidable medical, family, or other emergency, and
- You notify me prior to the due date
- Explain the information retrieval storage methods (Inverted Index and Signature Files)
- Explain retrieval models, such as Boolean model, Vector Space model, Probabilistic model, Inference Networks, and Neural Networks.
- Explain retrieval utilities such as Stemming, Relevance Feedback, N-gram, Clustering, and Thesauri, and Parsing and Token recognition.
- Design and implement a search engine prototype using the storage methods, retrieval models and utilities.
- Apply the research ideas into their experiments in building a search engine prototype
- a. An ability to apply knowledge of computing and mathematics appropriate to the discipline.
- c. An ability to design, implement and evaluate a computer-based system, process, component, or program to meet desired needs.
- d. An ability to function effectively on teams to accomplish a common goal.
- f. An ability to communicate effectively with a range of audiences.
- i. An ability to use current techniques, skills, and tools necessary for computing practices.
- j. An ability to apply mathematical foundations, algorithmic principles, and computer science theory in the modeling and design of computer-based systems in a way that demonstrates comprehension of the tradeoffs involved in design choices.
- k. An ability to apply design and development principles in the construction of software systems of varying complexity.