Study information

Generative AI Applications - 2025 entry

MODULE TITLEGenerative AI Applications CREDIT VALUE15
MODULE CODECOMM116 MODULE CONVENERUnknown
DURATION: TERM 1 2 3
DURATION: WEEKS 11
Number of Students Taking Module (anticipated) 40
DESCRIPTION - summary of the module content
Generative AI is driving innovative applications in different sectors. In this module, you will familiarise yourself with the foundations and essential tools of generative AI and focus on its key applications across various domains and their potential impact on industries and society. You will design and implement generative AI techniques for real-world applications in domains such as healthcare, environment, art, and entertainment. You will attend lecture and lab sessions, where you will learn to analyse and fine-tune generative models to optimise their performance for your chosen application(s). This module is suitable for Computer Science, Mathematics and Engineering students and any students with experience in programming and machine learning.
 
Pre-requisite modules: COMM113 Deep Learning
AIMS - intentions of the module

Building upon the machine learning foundations you have developed, this module aims to equip you with the knowledge, skills and tools to understand, analyse and apply Generative AI across various domains, including computer vision, natural language processing, and creative industries. You will explore real-world use cases, study the impact of generative models on different sectors, and develop hands-on experience with state-of-the-art tools, models and platforms. You will work with relevant AI frameworks and tools, for example, APIs of image generation models, to create generative AI-enabled applications. You will also learn ethical considerations, challenges, and emerging trends in Generative AI applications. By the end of this module, you will be equipped with the skills to design, implement, and critically evaluate Generative AI applications in both research and industry settings.

INTENDED LEARNING OUTCOMES (ILOs) (see assessment section below for how ILOs will be assessed)

On successful completion of this module you should be able to:

Module Specific Skills and Knowledge:

  1. Explain key applications of generative AI across various domains and their potential impact on industries and society.

  2. Optimise generative models for their performance, including efficiency and accuracy for various tasks.

Discipline Specific Skills and Knowledge:

  1. Design and implement generative AI techniques to solve real-world problems in domains such as healthcare, environment, art, and entertainment.

  2. Critically evaluate the performance and implications of generative AI technologies, such as ethical and societal implications in various domains.

  3. Identify the compromises and trade-offs which must be made when translating theory into practice. 

Personal and Key Transferable/ Employment Skills and Knowledge:

  1. Effectively communicate findings and evaluations drawn from research papers. 

SYLLABUS PLAN - summary of the structure and academic content of the module
Concepts and Theoretical Foundations
Introduction and history of Generative AI, the difference between generative models and discriminative models, and the development and breakthrough of generative AI technologies.
 
Implementation and Practical Techniques
Key generative models, for example, diffusion Models, GANs, and Large Language Models (LLMs)
Training and fine-tuning
Tools and frameworks for generative AI application development
 
Analysis and Evaluation
Quantitative and qualitative evaluation techniques  
Ablation study 
Real-world case studies and impact
LEARNING AND TEACHING
LEARNING ACTIVITIES AND TEACHING METHODS (given in hours of study time)
Scheduled Learning & Teaching Activities 33 Guided Independent Study 117 Placement / Study Abroad 0
DETAILS OF LEARNING ACTIVITIES AND TEACHING METHODS
Category  Hours of study time  Description 
Scheduled Learning & Teaching activities 22 Lectures
Scheduled Learning & Teaching activities 11 Workshops/tutorials
Guided independent study 60 Coursework preparation and completion
Guided independent study 57 Wider reading and self-study

 

ASSESSMENT
FORMATIVE ASSESSMENT - for feedback and development purposes; does not count towards module grade
Form of Assessment Size of the assessment e.g. duration/length ILOs assessed Feedback method
Practical Exercises  10 All Answers to exercises and oral feedback

 

SUMMATIVE ASSESSMENT (% of credit)
Coursework 100 Written Exams 0 Practical Exams 0
DETAILS OF SUMMATIVE ASSESSMENT
Form of Assessment % of credit Size of the assessment e.g. duration/length ILOs assessed  Feedback method
Continuous assessment 1 30  18 hours All Written
Continuous assessment 2 70  42 hours All Written

 

DETAILS OF RE-ASSESSMENT (where required by referral or deferral)
Original form of assessment Form of re-assessment  ILOs re-assessed Time scale for re-assessment
Continuous assessment 1 Continuous assessment 1 All Referral/deferral period 
Continuous assessment 2 Continuous assessment 2 All Referral/deferral period 

 

RE-ASSESSMENT NOTES

Reassessment will be by coursework/quiz in the failed or deferred element only. For referred candidates, the module mark will be capped at 50%. For deferred candidates, the module mark will be uncapped.

RESOURCES
INDICATIVE LEARNING RESOURCES - The following list is offered as an indication of the type & level of
information that you are expected to consult. Further guidance will be provided by the Module Convener
Basic reading:
  • Goodfellow, I., Bengio, Y., Courville, A. and Bengio, Y., 2016. Deep learning (Vol. 1, No. 2). Cambridge: MIT press.
  • Bishop, C.M. and Bishop, H., 2023. Deep learning: Foundations and concepts. Springer Nature.
  • Achiam, J., Adler, S., Agarwal, S., Ahmad, L., Akkaya, I., Aleman, F.L., Almeida, D., Altenschmidt, J., Altman, S., Anadkat, S. and Avila, R., 2023. Gpt-4 technical report. arXiv preprint arXiv:2303.08774.
  • Rombach, R., Blattmann, A., Lorenz, D., Esser, P. and Ommer, B., 2022. High-resolution image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 10684-10695).
Web-based and electronic resources: 

Reading list for this module:

There are currently no reading list entries found for this module.

CREDIT VALUE 15 ECTS VALUE 7.5
PRE-REQUISITE MODULES COMM113
CO-REQUISITE MODULES
NQF LEVEL (FHEQ) 7 AVAILABLE AS DISTANCE LEARNING No
ORIGIN DATE Monday 11th November 2024 LAST REVISION DATE Wednesday 6th August 2025
KEY WORDS SEARCH Generative AI, large language models, machine learning

Please note that all modules are subject to change, please get in touch if you have any questions about this module.