1. CS598CXZ (CS510) Advanced Topics in Information Retrieval (Fall 2016)
Instructor: ChengXiang (“Cheng”) Zhai
Teaching Assistants:
Rongda Zhu (full time)
Chase Geigle (part time)
Department of Computer Science
University of Illinois, Urbana-Champaign

2. Course Goal
Advanced (graduate-level) introduction to the field of information retrieval (IR), broadly including Text mining
Goal
Provide an in-depth introduction to advanced IR algorithms based on statistical language models
Provide an opportunity for students to explore frontier topics via course projects (customized toward the interests of students)
Give students enough training for doing research in IR or applying advanced IR techniques to applications
Tangible outcome: research paper, open source code, and application system

3. Prerequisites Basic concepts in CS410 Text Info Systems
Programming skills: CS225 or equivalent level
A good knowledge of basic probability and statistics
Knowledge of one or more of the following areas is a plus, but not required: Information Retrieval, Machine Learning, Data Mining, Natural Language Processing
Contact the instructor if you aren’t sure

4. Format
Mixture of
Lectures by instructor (about 50%)
Presentations by students (project-based) (about 50%)
Assignments: ensure solid mastery of concepts and skills of implementation
Written assignments + programming
Midterm (75 min, in class): mostly to verify your mastery of main concepts and algorithms
Course project: multiple options
In-depth study of a topic  publication/submission
Implementation of a major algorithm  open source
Development of a novel application  useful application

5. Office Hours Instructor: TA (0207SC?) Email us at any time
Tue. 1:30pm-2:30pm; Fri. 11:00am-12pm
2116SC
TA (0207SC?)
Rongda Zhu: Mon & Thur, am
Chase Geigle: TBD to accommodate online students
us at any time

6. Grading
Assignments: 30%
Midterm: 30%
Project: 40%

7. Schedule
Part I: Background, overview of IR research (lectures by instructors): relevant math
Part II: IR: frameworks and models (lectures by instructors)
Covering the major algorithms for optimizing ranking
Part III: Text analysis: topic models & neural language models (lectures by instructors)
Covering topic models and word embedding for text analysis
Part IV: Frontier topics and applications (project-based workshop; presentations by students + discussions)
Covering project-related frontier topics, system implementation, and applications

8. Presentation &discussion
Your Work Load
Aug
Sept
Oct
Nov
Dec
Aug 24
Thanks-
giving
Dec 6
Last Day
of Instruction
Readings
Assignments
Midterm
Paper/project
Presentation &discussion
Project

9. Reference Book
ChengXiang Zhai, Chase Geigle, Statistical Language Models for Text Data Retrieval and Analysis, forthcoming. Draft will be available online

10. Other readings: mostly research papers, survey articles, and book chapters
Synthesis Lectures Digital Library:
Foundations & Trends in IR:
Recent papers from SIGIR, CIKM, WWW, WSDM, KDD, ACL, ICML,…

11. Questions? Course website: http://times.cs.uiuc.edu/course/598f16
Piazza: