Module manager: Prof Vania Dimitrova
Email: V.G.Dimitrova@leeds.ac.uk
Taught: Semester 1 (Sep to Jan) View Timetable
Year running 2024/25
Students must study one of the below modules.
| COMP2611 | Artificial Intelligence |
This module is not approved as a discovery module
On completion of this module, students should be able to:
- apply human-computer interaction methodology to identify user needs, draw requirements, design, and evaluate user-adaptive systems;
- identify most common techniques for user modelling and adaptation and apply them in practical areas;
- implement one or more recommender system techniques in a practical application;
- reason about the significance of user-adaptive systems and directions the field is going to develop.
On completion of this module, students should be able to:
-understand and demonstrate coherent and detailed subject knowledge and professional competencies some of which will be informed by recent research/scholarship in the discipline;
-deploy accurately standard techniques of analysis and enquiry within the discipline;
-demonstrate a conceptual understanding which enables the development and sustaining of an argument;
-describe and comment on particular aspects of recent research and/or scholarship;
-appreciate the uncertainty, ambiguity and limitations of knowledge in the discipline;
-make appropriate use of scholarly reviews and primary sources;
-apply their knowledge and understanding in order to initiate and carry out an extended piece of work or project;
Skills outcomes
Experience and understanding of techniques for user modelling and their application to build user adaptive intelligent systems
Experience and understanding of techniques for user modelling and their application to build user adaptive intelligent systems
- Adaptable and adaptive systems;
- General architecture of user-adaptive systems (main components, role and importance, examples)
- User model representation and building (user model representation, user model building, explicit and implicit models, cold start, stereotypes)
- Recommender Systems (recommender systems types, content-based filtering, knowledge-based recommenders, collaborative filtering, hybrid recommender systems, adapting recommendations to groups, recommender systems evaluation)
- Adaptive content presentation (static and dynamic approaches for content adaptation)
- Evaluation of user-adaptive systems (layered evaluation, usability threats)
- Responsible personalisation (privacy, transparency, explanation, trust)
| Delivery type | Number | Length hours | Student hours |
|---|---|---|---|
| Lectures | 20 | 1 | 20 |
| Private study hours | 80 | ||
| Total Contact hours | 20 | ||
| Total hours (100hr per 10 credits) | 100 | ||
Recommended 40 hrs of private study, to include 20 hrs working on summative coursework and 20 hrs private study following lectures. Remaining hours to read the articles issued in lectures and revision for examination.
Progress is monitored through class exercises throughout the module and summative coursework.
| Assessment type | Notes | % of formal assessment |
|---|---|---|
| In-course Assessment | Coursework 1 | 40 |
| Total percentage (Assessment Coursework) | 40 | |
Normally resits will be assessed by the same methodology as the first attempt, unless otherwise stated.
| Exam type | Exam duration | % of formal assessment |
|---|---|---|
| Open Book exam | 2.0 Hrs 0 Mins | 60 |
| Total percentage (Assessment Exams) | 60 | |
This module will be reassessed by open book examination.
The reading list is available from the Library website
Last updated: 9/25/2024
Errors, omissions, failed links etc should be notified to the Catalogue Team