CE706-7-SP-CO:
Information Retrieval
2023/24
Computer Science and Electronic Engineering (School of)
Colchester Campus
Spring
Postgraduate: Level 7
Current
Monday 15 January 2024
Friday 22 March 2024
15
03 August 2023
Requisites for this module
(none)
(none)
(none)
CE306
(none)
MSC G51512 Big Data and Text Analytics
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 much 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 magnitudes larger than ever before (the rise of `big data`).
As a result of that new paradigms for storing, indexing and accessing information have emerged. This module will provide the essential foundations of information retrieval and equip the students with solid, applicable knowledge of state-of-the-art search technology. Students will write computer programs to solve IR tasks.
The module assumes a reasonable programming background and is not suitable for students without prior programming experience.
The aim of the module is to provide the essential foundations of information retrieval and equip the students with solid, applicable knowledge of state-of-the-art search technology. Through directed reading and via practical assignments the module also exposes students to a variety of advanced information retrieval techniques.
After completing this module, students will be expected to be able to:
1. Design and implement a standard information retrieval system
2. Understand standard IR models and their merits and limitations
3. Demonstrate practical knowledge of developing large-scale search applications
4. Demonstrate practical knowledge of employing state-of-the-art open source software for IR
5. Demonstrate an understanding of commonly used evaluation approaches in IR
6. Demonstrate practical knowledge of employing advanced concepts of search applications
The module assumes a reasonable programming background and is not suitable for students without prior programming experience.
Lectures, lab sessions and classes
The above list is indicative of the essential reading for the course.
The library makes provision for all reading list items, with digital provision where possible, and these resources are shared between students.
Further reading can be obtained from this module's
reading list.
Assessment items, weightings and deadlines
| Coursework / exam |
Description |
Deadline |
Coursework weighting |
| Coursework |
Progress Test 1 |
|
10% |
| Coursework |
Progress Test 2 |
|
10% |
| Coursework |
Assignment 1: Elasticsearch, Indexing and Text Processing |
|
40% |
| Coursework |
Assignment 2: Evaluation |
|
40% |
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
Reassessment
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
Yes
No
Yes
Dr Colin Johnson
University of Nottingham
Dr MARJORY CRISTIANY Da COSTA ABREU
Sheffield Hallam University
Senior Lecturer
Available via Moodle
Of 92 hours, 58 (63%) hours available to students:
30 hours not recorded due to service coverage or fault;
4 hours not recorded due to opt-out by lecturer(s), module, or event type.
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