CS707 Information Retrieval

Instructor: T. K. Prasad
Phone No.: (937)-775-5109
Email: t.k.prasad@wright.edu
Home Page: http://www.cs.wright.edu/~tkprasad

Quarter: Winter, 2009.
Class Hrs: 4:10pm-5:25pm, TTh, 171 Math&MicBio.
Office Hrs: 3:30pm-4pm and 5:30pm-6pm. 395 Joshi Research Center (or by appointment)

Course Objective

To cover the foundations of information retrieval, to enable design, analysis and implementation of IR systems.

Course Prerequisite

CS600 Data Structures and Algorithms

Course Description

This course covers models for information retrieval, techniques for indexing and searching, and algorithms for classification and clustering. It will also cover SVM, latent semantic indexing, link analysis and ranking, Map-Reduce architecture and Hadoop, to different degrees of detail, time permitting.

Course Load

The course load includes a programming project (30 pts), a midterm exam (30 pts), and a final exam (40 pts).

Required Texts

"Introduction to Information Retrieval". C.D. Manning, P. Raghavan, H. Schütze. Cambridge University Press, 2008.

Recommended Texts

"Modern Information Retrieval". Ricardo Baeza-Yates (Author), Berthier Ribeiro-Neto. Addison Wesley, 1999.
"Mining the Web: Discovering Knowledge from Hypertext Data". Soumen Chakrabarti. Morgan Kaufmann, 2003.
"Information Retrieval: Algorithms and Heuristics", D. Grossman and O. Frieder. Springer, 2nd Ed., 2004.
"Managing Gigabytes: Compressing and Indexing Documents and Images", I. Witten, A. Moffat, and T. Bell. 2nd Ed., Morgan Kaufmann, 1999.
"Understanding Search Engines: Mathematical Modeling and Text Retrieval", Michael W. Berry, and Murray Browne, 2nd Ed., SIAM, 2005.
"Search Engines: Information Retrieval in Practice", Addison Wesley, 2009.

Reference URLs

Lucene Text Search Engine (http://lucene.apache.org/)
Hadoop (http://hadoop.apache.org/core/)
Singular Value Decomposition (http://www.uwlax.edu/faculty/will/svd/index.html)
Information Retrieval and Web Mining (http://www.stanford.edu/class/cs276/cs276-2006-syllabus.html)
Strang's Linear Algebra Course (MIT) (http://ocw.mit.edu/OcwWeb/Mathematics/18-06Spring-2005/CourseHome/index.htm)
Andrew Moore's Statistical Data Mining Tutorials (CMU) (http://www.autonlab.org/tutorials/)

Grading

The A/B/C/D/F letter grade will be assigned at the end of the course.

Tentative Class Schedule and Syllabus

	Topics	Addl. Reading
Class 1	Information Retrieval; The Boolean Model	MIR-1
Class 2	The Vector Space Model : Term Weighting and Scoring	IIR-6, MIR-2
Class 3	Inverted Index Construction	IIR-1, MIR-8.2
Class 4	Dictionary and Postings; Query Processing	IIR-2, MIR-7.2
Class 5	Tolerant Retrieval (B-Trees)	IIR-3
Class 6	Index Construction	IIR-4, MG-5
Class 7	Map Reduce Architecture	Hadoop
Class 8	Index Compression	IIR-5, MG 3.3-4
Class 9	Vector Space Model : TF-IDF	IIR-6.2-4
Class 10	Midterm Exam (Feb 10)
Class 11	Vector Space Model : Ranking Revisited	IIR-6.1, IIR-7
Class 12	Evaluation in Information Retrieval	IIR-8, MIR-3 IIR-8
Class 13	Relevance Feedback and Query Expansion	IIR-9, MIR-5.2-4
Class 14	Text Classification and Naive Bayes	IIR-13
Class 15	Vector Space Classification	IIR-14
Class 16	Support Vector Machines	IIR-15, Primer
Class 17	Flat and Hierarchical Clustering	IIR-16, IIR-17
Class 18	Latent Semantic Indexing	IIR-18, Refs
Class 19	Linear Algebra: Matrix Decompositions	SVD-URL
Class 20	Wrap-Up
Class *	Web Characteristics	IIR-19
Class *	Web Search: Crawling and Indexes	IIR-20
Class *	Link Analysis	IIR-21
	Final Exam (5:45pm-7:45pm, March 17)

Assignments (Winter 2009)

Assignment 1 .

Exams (Winter 2009)

Midterm.
Final.

T. K. Prasad ( 18 Mar 2009 )