Computer Science and Electronic Engineering (School of)
Undergraduate: Level 6
Sunday 17 January 2021
Friday 26 March 2021
14 April 2020
Requisites for this module
BSC GH56 Information and Communication Technology,
BSC GH5P Information and Communication Technology (Including Year Abroad),
BSC I102 Information and Communication Technology (Including Placement Year),
BSC I1G3 Data Science and Analytics,
BSC I1GB Data Science and Analytics (Including Placement Year),
BSC I1GC Data Science and Analytics (Including Year Abroad),
BSC I1GF Data Science and Analytics (Including Foundation Year),
BSC LG01 Economics with Computing,
BSC LG02 Economics with Computing (Including Year Abroad),
BSC LG03 Economics with Computing (Including Placement Year),
BSC L310 Sociology with Data Science,
BSC L311 Sociology with Data Science (including Year Abroad),
BSC L312 Sociology with Data Science (including Placement Year),
BSC L313 Sociology with Data Science (including foundation Year)
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.
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.
On completion of the module, students will be expected to be able to:
- Design and implement a standard information retrieval system
- Understand standard IR models and their merits and limitations
- Demonstrate practical knowledge of employing state-of-the-art open source software for IR
- Demonstrate an understanding of commonly used evaluation approaches in IR
- Understand advanced concepts of search applications such as personalisation, profiling and contextual search
No additional information available.
Lectures, lab sessions and classes
- Croft, W. Bruce; Metzler, Donald; Strohman, Trevor. (c2010) Search engines: information retrieval in practice, Boston: Pearson Education.
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
||Assignment 1 - Week 21
||Assignment 2 - Week 25
Module supervisor and teaching staff
Dr Alba Garcia Seco De Herrera, email: email@example.com.
Dr Alba Garcia Seco De Herrera, Dr Shoaib Jameel
School Office, email: csee-schooloffice (non-Essex users should add @essex.ac.uk to create full e-mail address), Telephone 01206 872770
Dr Yunfei Chen
University of Warwick
Available via Moodle
Of 57 hours, 25 (43.9%) hours available to students:
32 hours not recorded due to service coverage or fault;
0 hours not recorded due to opt-out by lecturer(s).
* Please note: due to differing publication schedules, items marked with an asterisk (*) base their information upon the previous academic year.
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