![]() ![]() Quizzes: We will have several short in-class quizzes. This will be based on contribution outlined in status reports and the team assessment. However I reserve the right to give different grades to students in the same group if I feel that it is warranted. More details will be provided later in the quarter.Important Note: I expect that in most cases, everyone in the group will get the same grade. You may work in teams of three to build a information retrieval system. Project: There will be a team-based project toward the end of the quarter (instead of a final exam). I will usually handout a study guide the week before the midterm. We will have an in-class review the Tuesday before the midterm. There will no makeup exams unless you let me know of any conflicts ahead of time and bring a doctor’s note. Midterms: There will be two written in-class midterms during the quarter. There will be a deduction of 10% penalty for each day late. Assignments can be submitted up to amaximum of 3 days past the deadline. Always check the assignment page for due dates. Late Homework Policy: All of the assignments are to be submitted electronically. These assignments are meant to help you learn the theory covered in class by practical implementation of the concepts. As part of this philosophy, there will be a series of homework assignment sduring the quarter. I believe that students learn better by doing. ![]() Search User Interfaces, Cambridge University Press, September, 2009Īssignments: This course is designed to be a hands-on learning experience. ![]() Modern Information Retrieval the concepts and technology behind search Manning, Prabhakar Raghavan and Hinrich Schutze, Introduction to Information Retrieval, Cambridge University Press. Jure Leskovec, Anand Rajaraman, Jeff UllmanĬhristopher D. Search Engines: Information Retrieval in Practiceīruce Croft, Donald Metzler, Trevor StrohmanĪddison Wesley 1 edition (February 16, 2009) Questions relating to lecture or assignment should be posted to discussion board, not emailed to teachers, so any teacher/student can respond and fellow students benefit from answers. Lectures slides will be placed on a shared Google Drive: ĬampusWire will be used for discussions- announcements. Traditional and machine learning-based ranking approaches IR techniques for the web, including crawling, link-based algorithms, and metadata usage It will also cover latent semantic indexing, link analysis and ranking, Map-Reduce architecture and Hadoop, to different degrees of detail, time permitting.īoolean and vector-space retrieval models This course will cover models for information retrieval, techniques for indexing and searching, and algorithms for classification and clustering. Office Hours: M 12:30 - 1:30, TH 1 - 2, and by appointment ![]()
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