Please note: there may be some adjustments to the teaching arrangements published in the course catalogue for 2020-21. Given current circumstances related to the Covid-19 pandemic it is anticipated that some usual arrangements for teaching on campus will be modified to ensure the safety and wellbeing of students and staff on campus; further adjustments may also be necessary, or beneficial, during the course of the academic year as national requirements relating to management of the pandemic are revised.

Information Retrieval (M) COMPSCI5011

  • Academic Session: 2020-21
  • School: School of Computing Science
  • Credits: 10
  • Level: Level 5 (SCQF level 11)
  • Typically Offered: Summer
  • Available to Visiting Students: No
  • Available to Erasmus Students: No

Short Description

To present students with an in-depth examination of the theoretical and practical issues involved in providing tools to access large collections of documents, especially in the context of the World Wide Web.

To present students with the practical engineering issues raised by the design and implementation of an information retrieval system.

Timetable

Four days over two weeks (2 days/week).

It is no known yet which two weeks or which days this will be.

Requirements of Entry

COMPSCI4048 Programming and Systems Development (H)

Excluded Courses

None

Co-requisites

None

Assessment

Class test 80%, coursework 20%.

(We note that there is no option for a Class Test in the list above)

Course Aims

To present students with an in-depth examination of the theoretical and practical issues involved in providing tools to access large collections of documents, especially in the context of the World Wide Web.

To present students with the practical engineering issues raised by the design and implementation of an information retrieval system.

Intended Learning Outcomes of Course

By the end of the course students will be able to:

1. Implement a standard information retrieval (IR) system;

2. Discuss the theoretical basis behind the standard models of IR (e.g. Boolean, Vector-space, and Probabilistic models);

3. Discuss how an IR system should be evaluated in terms of the system's performance and the user's satisfaction with the system;

4. Understand the concepts behind the different retrieval models including advanced machine learning models such as learning to rank;

5. Understand the techniques involved in retrieving information from the World Wide Web;

6. Describe the practical engineering issues raised by the implementation of a search engine for the Web;

7. Understand techniques and architectures necessary to speed up the retrieval process for large-scale IR systems.

Minimum Requirement for Award of Credits

Students must submit at least 75% by weight of the components (including class tests) of the course's summative assessment.