This is a graduate-level course covering the major research topics in the growing field Information Retrieval (IR). Briefly speaking, IR is the underlying science of search engines. Although the impact of IR research is most visible from the recent dramatic growth of the Web search engine industry, the history of IR research actually dates back to several decades ago, and a large number of research papers have been published in the interdisciplinary field of computer science, library, and information science. The application of IR research also goes beyond search engines to text data mining, and intelligent information systems in general.
The goal of this course is: 1) to provide an overview of IR research in the past several decades, 2) to systematically review the core research topics, models, and algorithms in IR, 3) to read and discuss selected most recent research papers customized toward student interests, and 4) to give students enough training for doing research in IR or applying advanced IR technologies in practical applications.
The course will involve lectures by instructor, student presentations, and research projects on major research topics in information retrieval. Students are expected to read quite a few research papers, discuss them using a wiki space, and present some of them at the class. There will be a late midterm and a few assignments. Students are also required to finish a research project that can potentially lead to a publication or an innovative useful system. Group projects are allowed.
Prerequisite: Students are expected to have a good knowledge of basic probability and statisticcs in addition to programming skills at the level of CS225 or a similar programming course. Some background in one or more of the following areas: information retrieval, machine learning, natural language processing, data mining, or databases would be a plus, but not required. If you are not sure whether you have the right background, please contact the instructor.