CE706-7-SP-CO:
Information Retrieval

The details
2025/26
Computer Science and Electronic Engineering (School of)
Colchester Campus
Spring
Postgraduate: Level 7
Current
Monday 12 January 2026
Friday 20 March 2026
15
27 August 2024

 

Requisites for this module
(none)
(none)
(none)
CE306

 

(none)

Key module for

MSC G412JS Applications of Artificial Intelligence

Module description

Students are not permitted to undertake this module if they have done the equivalent module of CE306 at UG level on a CSEE undergraduate course. This is because it cannot count towards your course credits. Please contact the CSEE School Office for specific advice on your module choices.


Search engines have become the first entry point into a world of knowledge and they form an essential part of many modern computer applications. While many of the underlying principles have been developed over decades, the landscape in search engine technology has changed dramatically in recent years to deal with data sources which are magnitudes larger than ever before (the rise of ‘big data’).


As a result, new paradigms for storing, indexing and accessing information have emerged. This module will describe the essential foundations of information retrieval and equip the students with solid, applicable knowledge of state-of-the-art search technology.

Module aims

The aims of the module are:



  • To teach students how to design, build and evaluate an information retrieval system, and to provide the essential foundations of information retrieval

  • To describe key research issues and possible future developments

Module learning outcomes

By the end of this module, students will be expected to be able to:



  1. Design and implement a standard information retrieval system.

  2. Critically evaluate standard IR models and their merits and limitations.

  3. Demonstrate a comprehensive understanding of commonly used evaluation approaches in IR.

  4. Apply advanced concepts of search applications.

Module information

Indicative Syllabus:



  • Term Frequency

  • Inverted Document Frequency

  • Inverted Indexing

  • Processing Pipelines

  • Evaluation

  • Neural Network Approaches

  • Research Topics

Learning and teaching methods

This module will be delivered via:

  • Lectures, lab sessions and classes

Bibliography*

This module does not appear to have a published bibliography for this year.

Assessment items, weightings and deadlines

Coursework / exam Description Deadline Coursework weighting
Coursework   Progress Test, week 23 (invigilated in CSEE labs, closed book, on Moodle, MCQ)    25% 
Coursework   Coding Exercise    75% 
Exam  Main exam: In-Person, Open Book (Restricted), 120 minutes during September (Reassessment Period) 
Exam  Reassessment Main exam: In-Person, Open Book (Restricted), 120 minutes during January 

Exam format definitions

  • Remote, open book: Your exam will take place remotely via an online learning platform. You may refer to any physical or electronic materials during the exam.
  • In-person, open book: Your exam will take place on campus under invigilation. You may refer to any physical materials such as paper study notes or a textbook during the exam. Electronic devices may not be used in the exam.
  • In-person, open book (restricted): The exam will take place on campus under invigilation. You may refer only to specific physical materials such as a named textbook during the exam. Permitted materials will be specified by your department. Electronic devices may not be used in the exam.
  • In-person, closed book: The exam will take place on campus under invigilation. You may not refer to any physical materials or electronic devices during the exam. There may be times when a paper dictionary, for example, may be permitted in an otherwise closed book exam. Any exceptions will be specified by your department.

Your department will provide further guidance before your exams.

Overall assessment

Coursework Exam
40% 60%

Reassessment

Coursework Exam
40% 60%
Module supervisor and teaching staff
Dr Richard Sutcliffe, email: rsutcl@essex.ac.uk.
Dr Richard Sutcliffe
School Office, email: csee-schooloffice (non-Essex users should add @essex.ac.uk to create full e-mail address), Telephone 01206 872770

 

Availability
Yes
No
Yes

External examiner

Prof Stephen Smith
University of York
Professor and Head of School
Dr Muhammad Bilal
Lancaster University
Senior Lecturer
Resources
Available via Moodle
Of 1089 hours, 0 (0%) hours available to students:
1089 hours not recorded due to service coverage or fault;
0 hours not recorded due to opt-out by lecturer(s).

 

Further information

* Please note: due to differing publication schedules, items marked with an asterisk (*) base their information upon the previous academic year.

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