CS5604 - Information Retrieval

Alternate title: Search Engines and Text Mining

Alternate title: Applied Machine Learning (Underlying Google)

Why take CS5604?

• To prepare you for working at Google, Microsoft, or any company involved in machine learning, text analytics, searching, and/or WWW.
• To prepare you for research involving search engines, social media, natural language processing, text mining, classification, clustering, indexing, recommendation/personalization, web archiving, and/or information extraction/seeking/exploration.
• To gain proficiency with parallel processing on clusters with big nodes.

Resources

• 20+ node Hadoop Cluster with 10Gbit network connection
• Cloudera software including HBase, HDFS, Hive, Mahout, MapReduce, Nutch, Pig, Solr, Spark, Sqoop`
• MeTA, NLTK, and Python toolkits

References

• Textbook: Introduction to Information Retrieval by Christopher D. Manning, Prabhakar Raghavan, and Hinrich Schütze. 2008, 496 pages, Cambridge University Press, ISBN-10: 0521865719, ISBN-13: 978-0521865715. See also online versions, slides, etc.
• Free (through Library download), recommended: ChengXiang Zhai and Sean Massung. 2016. Text Data Management and Analysis: a Practical Introduction to Information Retrieval and Text Mining. Association for Computing Machinery and Morgan & Claypool, New York, NY, USA.
• VTechWorks reports from CS5604 projects

Course Organization

• CS5604 Fall 2017 class: CRN 82613, TR, 3:30-4:45pm, NCB 210, 15T
• Approach: problem/project based learning
• Goal: solve the following question: How can we best build a state-of-the-art information retrieval and analysis system in support of the GETAR (Global Event and Trend Archive Research) project (NSF IIS grant 1619028)?
• The students in the class will confront this driving question, working in teams, with the teams cooperating, but with each team focused on a particular collection of data (made up of both webpages and tweets, relating to a particular event or trend).
• The instructor and several GRAs working on related research will provide guidance and assistance.
• This is one of the courses leading to a XCaliber Award "for making extraordinary contributions to technology enriched active learning".

About the Instructor

• Professor Edward A. Fox, fox@vt.edu, 540-231-5113, Torg. 2160G. Office hours are Wed 2-5, Thu 1-3, or by appointment.
• Dr. Fox is an IEEE Fellow: for leadership in digital libraries and information retrieval
• Dr. Fox's 1983 Ph.D. was supervised by Prof. Gerard Salton at Cornell University, often called "the father of information retrieval".
• GRAs, working in 2030 Torg.:
  • Liuqing Li, liuqing@vt.edu
  • Xuan Zhang, xuancs@vt.edu