1. CS598CXZ (CS510) Advanced Topics in Information Retrieval (Fall 2014) Instructor: ChengXiang (“Cheng”) Zhai 1 Teaching Assistants: Xueqing Liu, Yinan Zhang Department of Computer Science University of Illinois, Urbana-Champaign

2. Course Goal Advanced (graduate-level) introduction to the field of information retrieval (IR) Goal –Provide an overview of IR research in the past several decades –Systematically review the core research topics in IR –Discuss the most recent research progress (customized toward the interests of students) –Give students enough training for doing research in IR or applying advanced IR techniques to applications More in-depth treatment of topics than CS410: less emphasis on practical skills, more on understanding of principles, models, and algorithms 2

3. Prerequisites 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 3

4. Format Mixture of –Lectures by instructor (about 50%) –Presentations by students (about 25%) –Project-based workshop (about 25%) Assignments: mostly to ensure mastering of core IR topics –Frequent written assignments + 1 exploratory (programming + experiments) Midterm (75 min, in class): mostly to verify that you have mastered all the assignments Course project: in-depth study of a topic, aiming at a publication/submission or a novel useful system or a novel test collection 4

5. Office Hours Instructor: –Tue. 10am-11am; Thur. 3:30-4:30pm –2116SC TA: all in 0207SC –Xueqing Liu: Fri: 3-4pm –Yinan Zhang: Mon: 9-10am Email us at any time 5

6. Grading Assignments: 40% Midterm: 20% Project: 40% 6

7. Schedule Part I: Background, overview of IR research (lectures by instructors) –Historical overview; relevant math; ML; NLP Part II: IR: frameworks and models (lectures by instructors) –Covering the major algorithms for optimizing ranking Part III: Text mining: statistical topic models (lectures by instructors) –Covering topic models for text mining Part IV: Frontier IR topics (project-based workshop; presentations by students + discussions) –Covering project-related frontier topics; in-class problem solving 7

8. Your Work Load Assignments Aug 26 Project Aug Sept Nov OctDec Readings Midterm Paper presentation /discussion Dec 10 Last Day of Instruction Thanks- giving 8

9. Readings: mostly research papers, survey articles, and book chapters –Synthesis Lectures Digital Library: http://www.morganclaypool.com/http://www.morganclaypool.com/ –Foundations & Trends in IR: http://www.nowpublishers.com/ir/http://www.nowpublishers.com/ir/ –Recent papers from SIGIR, CIKM, WWW, WSDM, KDD, ACL, ICML,… 9

10. How to Get the Most from 598CXZ? Attend every lecture and make sure to understand the slides Read all the assigned readings Active learning –Read in advance –Actively participate in discussion (in class & with wiki) Collaborative learning (discussion with peers) Practice critical and creative thinking 10

11. Questions? Course website: http://times.cs.uiuc.edu/course/598f14 11