CSC-40070 - Applications of AI, Machine Learning and Data Science
Coordinator: Edward J De Quincey Tel: +44 1782 7 34090
Lecture Time: See Timetable...
Level: Level 7
Credits: 15
Study Hours: 150
School Office: 01782 733075

Programme/Approved Electives for 2021/22

None

Available as a Free Standing Elective

No

Co-requisites

None

Prerequisites

None

Barred Combinations

None

Description for 2021/22


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

None

Description of Module Assessment

1: Report weighted 70%
Report outlining an investigation into an authentic "big data" problem, using AI and data science techniques
Students will be asked 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: Presentation weighted 30%
Presentation of a "big data" investigation and reflection on legal, ethical and social aspects encountered
Students will individually present 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.