ITE 917 – Information Retrieval

This course expects the Ph.D. student to review background material in the area of Information Retrieval, Extraction, and Management, and to develop/deliver a presentation on a research topic. The course gives an up-to-date treatment of all  aspects of the design and implementation of systems for gathering, indexing, and searching documents; methods for evaluating systems; and an introduction to the use of machine learning methods on text collections. Topics include: Boolean retrieval, term vocabulary and postings lists, dictionaries, and tolerant retrieval, index construction and index compression, scoring, term weighting, and the vector space model, computing scores in a complete search system, evaluation in information retrieval, relevance feedback, and query expansion.

Course Objectives

The main objective of this course is to present and be able to describe the different advance concepts in information retrieval and more advance techniques of multimodal based information systems. The second objective of the course for the student is to understand the underlined problems related to IR and acquire the necessary experience to design, and implement real applications using Information Retrieval systems.


Bioinformatics is a new multidisciplinary field which utilizes computer science, information technology and mathematics to tackle and answer biology problems. This course has been designed for students with life sciences background who are interested in bioinformatics to allow them to know how to search, retrieve, and analyze biological data. The students will learn how to use public bio-data to design their experiments and to analyze their lab results using different bioinformatic tools. A problem-based learning approach will be implemented to ensure that students will gain enough hands on experience with various bioinformatic resources and to enhance their self learning capacities. This course will focus on fundamental domains in bioinformatics such as biological databases and sequence analysis with emphasis on exemplar problems from the different research groups of the master program.