CSC-40070 - Module Specification
School of Computer Science and Mathematics
Faculty of Natural Sciences

Programme/Approved Electives for 2024/25

Available as a Free Standing Elective

Co-requisites

None

Prerequisites

None

Barred Combinations

None

Description for 2024/25

This module covers a variety of cutting edge AI and Machine Learning techniques applied to authentic problems and datasets, giving students the opportunity to explore how these techniques can be contextualised in an area that they wish to pursue post-graduation. This will include areas and techniques such as decision making and behaviour control; reinforcement learning and deep learning; machine learning applied to areas such as Social Media; recommender systems used for entertainment and e-Commerce; voice and facial recognition and legal, ethical and social aspects of AI and Machine Learning.

This module aims to equip students with knowledge and experience of a variety of cutting edge AI and Machine Learning techniques applied to authentic problems and datasets. It also aims to give students the opportunity to explore how these techniques can be contextualised in an area that they wish to pursue post-graduation.

Intended Learning Outcomes

conduct complex investigations using statistical modelling, machine learning and deep learning techniques to make data driven decisions to solve authentic problems
: critically evaluate the efficacy of AI, machine learning and data science techniques in areas such as data mining, complex games and voice recognition: appraise the legal, ethical and social aspects of applications of AI, machine learning and data science:

Study hours

24 hours lectures/tutorials
12 hours of practicals
40 hours of coursework preparation
10 hours of presentation preparation
64 hours of guided independent study

School Rules

Knowledge of Programming is essential. Students not having a background in Programming are required to attend the course CSC-40044 (System Design and Programming) offered by the department.

Description of Module Assessment