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. The impact of IR research is most visible from the recent dramatic growth of the Web search engine industry, but applications of IR research also go beyond search engines to text data mining, and intelligent information systems in general.
The goal of this course is: 1) to provide a systematic review of the major research progress in information retrieval and text data mining with an emphasis on probabilistic retrieval models, statistical language models, and applications of machine learning; 2) to provide the students with an opportunity to learn frontier topics through working on carefully designed course projects and reading relevant papers; and 3) 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. There will be frequent but short assignments and a late midterm exam. 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 and encouraged.
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.