CS7800 Information
Retrieval (Spring 2020) 
- Instructor: Keke Chen
- Phone No.: (937)-775-4642
- Email: keke.chen@wright.edu
- Home Page: http://www.cs.wright.edu/~keke.chen
- Semester: Spring, 2020
- Class Hrs: 3:35-4:30pm, MWF, Russ
339
- Office Hrs: 2-3 pm MW 385 Joshi
Research Center (or by appointment)
Course Objective
- To cover the foundations of information retrieval, the
design, analysis and implementation of IR systems, and text mining
methods.
Course Prerequisite
- CS3100/CS5100 Data Structures and Algorithms, OR
- CS7200
Algorithm Analysis and Design
Course Description
This
course will cover models for information retrieval, techniques for indexing and
searching, and algorithms for text mining. It will also cover core algorithms
such as SVM, latent semantic indexing, link analysis and ranking, Map-Reduce
architecture and Hadoop, and applications of deep learning in IR/text-related
tasks, to different degrees of detail, time permitting.
Course Load
The
course load includes programming projects, reading assignments, and a take-home
final exam.
Note that you will need significant
programming skills (Python is preferred) to finish the projects.
(1) You are encouraged to work on the project assignments in teams of two students. You can discuss and share
your work with the team members to improve your understanding of the material,
but not with other teams.
(3) The students will be prohibited from using any computer (e.g., laptop),
calculator, or cell phone during the exam.
(4) Any academic impropriety during an exam (which includes copying from other
students, or accessing web resources and passing it off as your work) or on the
assignments will have a minimum penalty of 'F' grade, plus additional
disciplinary action for unethical behavior. See http://www.wright.edu/students/judicial/integrity.html
for details.
2 Project Assignments (50%)
2-4 Reading Assignments (20%)
Take-home Final Exam (30%)
Required Texts
- "Introduction
to Information Retrieval" C.D. Manning, P. Raghavan, and H. Schütze. Cambridge University Press, 2008.
Recommended Texts
- "Modern
Information Retrieval". Ricardo Baeza-Yates
and 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", W. B. Croft, D.
Metzler, and T. Strohman, Addison Wesley, 2009.
- "Information
Retrieval : Implementing
and Evaluating Search Engines", Stefan Büttcher,
Charles L. A. Clarke, and Gordon V. Cormack, MIT Press, 2010.
- "Networks,
Crowds, and Markets", David Easley and Jon Klienberg,
Cambridge University Press, 2010.
- "Analyzing the Social Web",
Jennifer Golbeck, Morgan Kaufmann, 2013.
Reference URLs
- Lucene Text Search Engine (http://lucene.apache.org/)
- Hadoop (http://hadoop.apache.org/core/)
- Singular Value Decomposition (http://websites.uwlax.edu/twill/svd/)
- Strang's Linear Algebra Course (MIT) (http://ocw.mit.edu/OcwWeb/Mathematics/18-06Spring-2005/CourseHome/index.htm)
- SVM Quora (https://www.quora.com/What-
are-Kernels-in-Machine- )Learning-and-SVM - Andrew Moore's Statistical Data Mining Tutorials (CMU)
(http://www.autonlab.org/tutorials/)
- Matei Zaharia's
Introduction to MapReduce and Hadoop (Cloud Computing) (in
powerpoint) or (archived video)
Grading
The
A/B/C/D/F letter grade will be assigned at the end of the course. The
final grades may be curved according to the overall grade (projects + exams)
distribution.
Tentative Class Schedule
|
|
Topics |
Additional Reading |
|
1. |
Information Retrieval; The
Boolean Model |
IIR-1 |
|
2. |
The Vector Space Model : Term Weighting and Scoring |
IIR-6 |
|
3. |
Inverted Index Construction |
IIR-1 |
|
4. |
Dictionary and Postings;
Query Processing |
IIR-2 |
|
5. |
Tolerant Retrieval
(B-Trees) |
IIR-3 |
|
6. |
Index Construction |
IIR-4 |
|
7. |
Map Reduce Architecture |
|
|
8. |
Index Compression |
IIR-5 |
|
9. |
Vector Space Model : TF-IDF |
IIR-6.2 |
|
10. |
Vector Space Model : Ranking Revisited |
IIR-6.1 |
|
11. |
Midterm |
|
|
12. |
Evaluation in Information
Retrieval |
IIR-8 |
|
13. |
Relevance Feedback and
Query Expansion |
IIR-9 |
|
14. |
Text Classification and
Naive Bayes |
IIR-13 |
|
15. |
Vector Space Classification |
IIR-14 |
|
16. |
Support Vector Machines |
IIR-15, SVM |
|
17. |
Flat and Hierarchical
Clustering |
IIR-16, IIR-17 |
|
18. |
Latent Semantic Indexing |
IIR-18, Refs |
|
19. |
Linear Algebra: Matrix
Decompositions |
|
|
20. |
Link Analysis |
IIR-21 |
|
21. |
Other
topics |
|
|
Final Exam |
|