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

For academic year: 2023/24 Last Updated: 13 February 2024

CSC-40070 - Applications of AI, Machine Learning and Data Science
Coordinator: Pratik Shrestha
Lecture Time: See Timetable...
Level: Level 7
Credits: 15
Study Hours: 150
School Office: 01782 733075

Programme/Approved Electives for 2023/24

None

Available as a Free Standing Elective

No

Co-requisites

None

Prerequisites

None

Barred Combinations

None

Description for 2023/24

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.

Aims
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

1: Project weighted 70%
Report outlining an investigation into an authentic "big data" problem, using AI and data science techniques
Students will be asked to work in a group and to source a publicly available large dataset in an area that they are interested in pursuing as a future career and create a set of problem statements that they can investigate (related to the dataset). They will then apply techniques they have been taught as part of this module (and previous modules on the course where appropriate) to this dataset to try and solve the problems identified (formative feedback will be given at this stage to ensure the problems are appropriate/solvable). The report will contain an explanation of the stages their investigation, including the set of problem statements, data sourcing, cleaning and preparation, the techniques used (and why), the solutions to the problems, a reflection on the efficacy of their chosen techniques and appendices with code/results (or link to online repository/Jupyter Notebook). This will be the equivalent of a 3,000 word report.

2: Coursework weighted 30%
Presentation of a "big data" investigation and reflection on legal, ethical and social aspects encountered
Students will give group presentations about their investigation from Assessment 1 to the cohort. Each student will identify who the target audience of the presentation is and make suitable communication/presentation style choices (which will form part of the marking criteria). The presentation must also contain a reflection on the legal, ethical and social aspects they encountered/identified during their investigation. The presentation will be 8 minutes with 2 minutes for questions.

Please note that for the 2022/23 Academic Year programmes may be adapted to operate in accordance with a flexible, digital approach. This means that there may be some changes made to how modules are delivered and assessed. The latest information will be provided to you by your School in module handbooks. We endeavour to give you as much notice as possible of any changes and 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/student-terms-and-conditions/

Last Updated: 13 February 2024