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CSC-44112 - Module Specification
School of Computer Science and Mathematics
Faculty of Natural Sciences

For academic year: 2025/26 Last Updated: 04 December 2025

CSC-44112 - Advanced Applications of AI and Machine Learning
Coordinator: Baidaa Al-Bander
Lecture Time: See Timetable...
Level: Level 7
Credits: 30
Study Hours: 300
School Office: 01782 733075

Programme/Approved Electives for 2025/26

None

Available as a Free Standing Elective

No

Co-requisites

None

Prerequisites

None

Barred Combinations

None

Description for 2025/26

During interactive lectures and practicals, delivered by experts from the university and industry, you will learn to master methods such as exploratory data analysis, machine learning, and neural networks with common frameworks like PyTorch and TensorFlow. You will also encounter concepts such as deep learning, natural language processing, and computer vision as well as generative models which drive technologies such as ChatGPT. We will then explore how these techniques can be contextualised for your future career.

Aims
This module aims to enable students to:
1. Understand and apply key data science skills, such as Exploratory Data Analysis, Machine Learning, and Neural Networks using popular frameworks.
2. Explore advanced topics, such as Deep Learning, Natural Language Processing, Computer Vision, and Generative Models.
3. Apply theoretical knowledge practically, by providing hands-on experience through interactive lectures and practical sessions.
4. Contextualize techniques for career development, by exploring how data science techniques can be applied in various professional contexts to prepare students for their future careers.

Intended Learning Outcomes

apply key concepts of Exploratory Data Analysis (EDA) to prepare and analyse datasets for machine learning: 1,2
assess the suitability of machine learning algorithms for various datasets and problem domains, considering computational efficiency and interpretability: 1,2
apply data science tools and libraries for data visualisation and machine learning to solve authentic problems: 1,2
evaluate the impact of advanced AI techniques on real-world applications: 1
demonstrate the knowledge, skills and behaviours of a professional data scientist and identify required future learning: 1

Study hours

24 hours interactive lectures
46 hours practicals
60 hours guided study
170 hours independent study:
96 hours of independent practice/exploration
18 hours of weekly tasks
12 hours reflections on weekly tasks
44 hours Technical Report preparation

School Rules

Knowledge of Programming is essential. Students not having a background in Programming are required to attend the module CSC-44102

Description of Module Assessment

1: Portfolio weighted 30%
Portfolio of weekly tasks
Students will be set 6 weekly tasks to complete (practical labs, reading, online tutorials) that relate to the content delivered that week. A digital template will be made available where students evidence their completion of these tasks along with a short reflection. In each reflection, the students will use a reflective learning model to support them in reflecting on their learning experience, describing what they have learnt, identifying their strengths and identifying next steps for their learning. The final submission will include an overall reflection on what this means for their current and future career development. The portfolio is 1,500 word equivalent.

2: Report weighted 70%
Technical Data Science Report
A Technical Report summarising the analysis of a dataset (of the student’s choosing) with appropriate visualisations, supporting images and submission of code, to demonstrate the practical skills developed during the module. The Technical Report should reflect the key elements of a professional data science report and is 3,500 word equivalent.

The University undertakes a continuous review of its teaching and research provision to ensure programmes are of a high quality and changes may be made to modules where it is necessary and reasonable to do so. Please be aware that minor changes to modules, such as a change to an individual assessment, can occur up until enrolment, although we endeavour to give you as much notice as possible of any changes. Details about the circumstance in which changes are made to programmes can be found in the Student Agreement, available at: https://www.keele.ac.uk/legalgovernancecompliance/governance/actcharterstatutesordinancesandregulations/studenttermsconditions/

Last Updated: 04 December 2025