Efficient Text Indexing Models and Web Search Techniques
Learn about efficient text indexing, retrieval models (Boolean, vector-space, probabilistic, machine learning), evaluation methods, document clustering, web search algorithms, and more in this comprehensive course on Information Retrieval by Christopher Manning and Pandu Nayak at Stanford University.
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Presentation Transcript
Introduction to Information Retrieval Introduction to Information Retrieval CS276 Information Retrieval and Web Search Christopher Manning and Pandu Nayak Lecture 1: Introduction
Introduction to Information Retrieval Course logistics in brief Instructors: Christopher Manning and Pandu Nayak TAs: Ashwin Paranjape (Head TA) Chris Chute Fei Jia Rohan Sampath Time: TuTh 4:30 5:50, Gates B01 ( SCPD video) Class webpage: http://cs276.stanford.edu/ Will have office hours, etc. (starting next week) 2
Introduction to Information Retrieval Work for the class Required work: 2 problem sets @ 10% = 20% 3 programming assignments, 3rd one larger than first two: @ 13%, 13%, 20% = 46% Final exam: 30% Class participation: 4% 2% for (on-campus) attending guest lectures in person or (SCPD, unavoidable absences) writing reaction paragraph 2% for Piazza participation, mid-quarter survey completion, and/or being present and active in class Problem sets and assignments Due at: 4pm, either Tue (early in course) or Thu (later on) Assignments in Python this year 3
Introduction to Information Retrieval What do we hope to teach? How to do efficient (fast, compact) text indexing Retrieval models: Boolean, vector-space, probabilistic, and machine learning models Evaluation and IR interface issues Document clustering and classification Search on the web, including crawling, link-based algorithms, indirect feedback, metadata, and personalization 4
