Course Obejctives and Topics From Other Professors

Professor Eugene Agichtein. CS572: Information Retrieval and Web Search at Emory (spring 2010)

Basic and advanced techniques for text-based information systems: text indexing; Boolean, vector space, and probabilistic retrieval models; evaluation, feedback, user modeling, and interface issues; Web search including crawling, link-based algorithms, and Web metadata; text/Web clustering, classification; information extraction and text mining; Web2.0: Social networks, community systems, and user-generated content.

Professor James Allan. CMPSCI 646: Information Retrieval at UMass (fall 2010)

Core topics include material necessary to understand how an IR system is constructed and functions -- in particular, the material needed to carry out the class programming assignments. The following topics will be covered, though the order will be determined in part by student interest and class discussion: Evaluation, Retrieval models, Statistics of text, Indexing models, File organization, Efficiency, possibly including compression, Clustering, Relevance feedback, Document filtering, Distributed retrieval, Web search, Question answering, Multimedia retrieval, Cross-language retrieval, Advanced evaluation issues, Interactive retrieval, Interaction with Natural Language Processing

Professor William Y. Arms. Information Retrieval at Cornell (fall 2007)

This course looks at the methods used to search for and retrieve information from collections of documents, including Web search systems and library catalogs. The course combines theoretical and practical approaches, and includes sections on user interfaces and evaluation of the effectiveness of information retrieval systems.

Dictionaries, inverted files, postings, Term weighting, Similarity, ranking and the vector space model, String processing 1: Wild cards, stemming, and spelling, String processing: String search, Relevance feedback and query refinement, Latent semantic indexing, Probabilistic information retrieval, Evaluation of retrieval effectiveness, Web crawling, Architecture of information retrieval systems, Links and anchor text, Spam and advertising, Interfaces for browsing and searching, Metadata, Classification and categorization.

Professors Jamie Callan and Yiming Yang. 11-741: Information Retrieval at CMU (spring 2011)

This course studies the theory, design, and implementation of text-based information systems. The Information Retrieval core components of the course include statistical characteristics of text, representation of information needs and documents, several important retrieval models (Boolean, vector space, probabilistic, inference net, language modeling, link analysis), clustering algorithms, collaborative filtering, automatic text categorization, and experimental evaluation. The software architecture components include design and implementation of high-capacity text retrieval and text filtering systems.

Professor Ernest Davis. G22.2580: Web Search Engines at New York University (fall 2007)

We will discuss the design of a Web search engine and the extraction of information off the Web. Topics include: Web crawlers, Database design, Query language, Relevance ranking, Document Similarity and Clustering, The "invisible" Web, Specialized search engines, Evaluation, Natural Language Processing, The structure of the web, Intelligent retrieval and the semantic Web, Web content mining, Web usage mining, Multi-media retrieval, Multilingual retrieval.

Professor Brian Davison. CSE345/445: WWW Search Engines Algorithms, Architectures and Implementations at Lehigh University (Spring 2009)

This course focuses on the technologies for storing and retrieving large-scale hypertext datasets. Particular emphasis is given to the data structures and algorithms needed to build efficient search engines for the World Wide Web (WWW). Topics covered include: information retrieval (IR) models, performance evaluation, query languages and operations, properties of hypertext, crawling, indexing, searching, ranking, link analysis, parallel and distributed IR, and user interfaces. Students will participate in class projects involving both the creation and management of a large document collection on the WWW. These projects will require programming in languages such as Perl/CGI, C/C++, or Java.

Professor C. Lee Giles. IST 441: Information Retrieval and Search Engines at Penn State (spring 2012)

This course will introduce students to the principles of information storage and retrieval systems and databases. Students will learn how effective information search and retrieval is interrelated with the organization and description of information to be retrieved. Students will also learn to use a set of tools and procedures for organizing information, will become familiar with the techniques involved in conducting effective searches of print and online information resources and will build a vertical/specialty search engine.

Professor Mark Levene. Search Engines and Web Navigation at Birkbeck University of London (fall 2011)

Course content: The structure of the web - web metrics, Find information on the web - search and navigation, How people use the web - web data mining.

Professor Frank McCown. COMP 475: Search Engine Development at Harding (spring 2009)

The purpose of this class is understand the how the Web is organized, understand the characteristics and limitations of web search, develop several of the components to implement a web search engine, and make a significant contribution to an open source search engine project.

Professor Paul McNamee. 605.744: Information Retrieval at Johns Hopkins (spring 2011)

This course covers the storage and retrieval of unstructured digital information. Topics include automatic index construction, retrieval models, textual representations, efficiency issues, search engines, text classification, and multilingual retrieval.

Professor Rada Mihalcea. CSCE 5200 Information Retrieval and Web Search at University of North Texas (spring 2011)

This course will cover traditional material, as well as recent advances in Information Retrieval (IR), the study of indexing, processing, and querying textual data. Basic retrieval models, algorithms, and IR system implementations will be covered. The course will also address more advanced topics in "intelligent" IR, including Natural Language Processing techniques, and "smart" Web agents.

Dr. Raymond Mooney. Intelligent Information Retrieval and Web Search Course at UT Austin (fall 2011)

This course will cover traditional material as well as recent advances in information retrieval (IR), the study of the processing, indexing, querying, organization, and classification of textual documents, including hypertext documents available on the world-wide-web.

Professors Pandu Nayak and Prabhakar Raghavan. CS 276: Information Retrieval and Web Search at Stanford (spring 2011)

Basic and advanced techniques for text-based information systems: efficient text indexing; Boolean and vector space retrieval models; evaluation and interface issues; Web search including crawling, link-based algorithms, and Web metadata; text/Web clustering, classification; text mining.

Professor Hinrich Schutze. Introduction to Information Retrieval at Stuttgart (summer 2009)

Boolean retrieval, The term vocabulary and postings lists, Dictionaries and tolerant retrieval, Index construction, Index compression, Scoring, term weighting and the vector space model, Computing scores in a complete search system, Evaluation in information retrieval, Text classification and Naive Bayes, Vector space classification, Flat clustering, Hierarchical clustering, Web search basics, Web crawling and indexes, Link analysis

Professor Luo Si. CS 490 WIR: Web Information Retrieval and Management at Purdue (fall 2011)

This course studies the basic principles and practical algorithms used for those Web Information Systems. The contents include: Web search, recommendation system, Web information extraction, etc. The course emphasizes both the above applications and solid modeling techniques that can be extended for other applications. Students will: Learn the techniques behind Web search engines, E-commerce recommendation systems, etc.; Get hands on project experience by developing real-world applications, such as building a small-scale Web search engine, a Web page management system, or a movie recommendation system; Learn tools and techniques to do research in the area of information retrieval or text mining; Lead to the amazing job opportunities in Search Technology and E-commerce companies such as Google, Microsoft, Yahoo! and Amazon.

Dr. George V. Wilson. Information Retrieval at Georgetown (spring 2008)

The course is a survey of principles and techniques in information retrieval with a focus on text databases, including automatic indexing, search techniques, query mechanisms (Boolean queries, topic hierarchies, natural language queries), relevance feedback, and evaluation methodology. Students will examine the performance of selected commercial and web-based systems.

Dr. David Yarowsky. Information Retrieval and Web Agents at Johns Hopkins University (spring 2011)

Information Retrieval - Topics include a comprehensive study of current document retrieval models, mail/news routing and filtering, document clustering, automatic indexing, query expansion, relevance feedback, user modelling, information visualization and usage pattern analysis.

Text Understanding - This segment of the course will focus on additional language processing steps for template filling and information extraction from retrieved documents, including reference resolution, sense tagging and summarization. Emphasis will be placed on recent, primarily statistical methods.

Web Agents and WWW Applications - The final segment of the course will explore current issues in information retrieval and data mining on the World Wide Web. It will focus on case studies of web agents, spiders, robots and search engines, exploring both their practical implementation and the economic and legal issues surrounding their use. One of the hot technologies of the 21st century!