Transcript A Risk Minimization Framework for Information Retrieval

CS598CXZ (CS510)
Advanced Topics in Information Retrieval
(Fall 2014)
Instructor: ChengXiang (“Cheng”) Zhai
Teaching Assistants:
Xueqing Liu, Yinan Zhang
Department of Computer Science
University of Illinois, Urbana-Champaign
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Course Goal
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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
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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
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Format
Mixture of
– Lectures by instructor (about 50%)
– Presentations by students (about 25%)
– Project-based workshop (about 25%)
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Assignments: mostly to ensure mastering of core IR
topics
– Frequent written assignments + 1 exploratory (programming
+ experiments)
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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
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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
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Grading
• Assignments: 40%
• Midterm: 20%
• Project: 40%
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Schedule
Part I: Background, overview of IR research (lectures by
instructors)
– Historical overview; relevant math; ML; NLP
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Part II: IR: frameworks and models (lectures by
instructors)
– Covering the major algorithms for optimizing ranking
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Part III: Text mining: statistical topic models (lectures by
instructors)
– Covering topic models for text mining
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Part IV: Frontier IR topics (project-based workshop;
presentations by students + discussions)
– Covering project-related frontier topics; in-class problem
solving
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Your Work Load
Aug
Aug 26
Readings
Sept
Oct
Nov
Thanksgiving
Dec
Dec 10
Last Day
of Instruction
Assignments
Midterm
Paper presentation
/discussion
Project
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Readings: mostly research papers,
survey articles, and book chapters
– Synthesis Lectures Digital Library: http://www.morganclaypool.com/
– Foundations & Trends in IR: http://www.nowpublishers.com/ir/
– Recent papers from SIGIR, CIKM, WWW, WSDM, KDD, ACL, ICML,…
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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
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Questions?
Course website:
http://times.cs.uiuc.edu/course/598f14
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