CSE 494/598 Lecture Notes

1. Introduction (1/20;)
2. Text retrieval; vectorspace ranking
3. Indexing/Retrieval (1/22;)
4. Correlation analysis; LSI (2/3;2/5)
5. Search engine technology (2/10;2/12;2/16)
6. Page rank computation; Crawling; Anatomy of a search engine (2/19;)
7. Clustering (2/26;)
8. Collaborative and Content-based Filtering(3/4;3/11); Classification Learning (NBC);( Text classification (Vector vs. unigram models of text); Spam mail classification.(3/22;3/25)

9. A web-oriented review of Databases (Given by Ullas Nambiar)

10. XML

11. Semantic web and its standards...

12. Data/Information Integration

13. Learning Sources Stats (BibFinder)

14. The DB/IR intersection

15. Final class

1/20;1/22: intro; vector space
1/27;1/29: vectorspace
2/3;2/5:  correlation; LSI
2/10;2/12: Search engine tech: A/H
2/16;2/20: Search engine tech: Page rank; practicial considerations

2/24;2/26 --Google; Crawling; Clustering
3/2;3/4  --Clustering 2; collaborative filtering
3/9:Midterm; 3/11 --Mid-term discussion; Content based filtering;
start of classification learning. 
SPRING BREAK
3/23--Naive Bayes Classification& Text Classification; and Spam
Filtering (3/25)
3/30: DB refresher;4/1: XML
4/6 Xquery usecases; semantic web;4/8Information Integration start
4/13;4/15  Over view of Info. vs. Data integration; Issues in DI
(vs. DB and DDB); issues in DB/IR.;;; Project 2 discussion; DI Models (GAV vs. LAV)
4/20;4/22 GAV/LAV models; Query optimization in Data Integration 
4/27;4/29 Bibfinder; DB/IR
5/4: Interactive Semester Review