CET313 - Artificial Intelligence

Assessment Brief

Intelligent Prototype Development

1. Specification

This assignment is weighted at 100% of the overall module and will be marked out of 100. This assessment requires approximately 40 hours to complete.

The aim of this assessment is to provide you with an opportunity to demonstrate your understanding and practical skills in Artificial Intelligence. You must propose your own project concept, subject to approval by the Module Leader. The assessment is designed to assess your ability to develop a small prototype, evaluate its performance, and compile a comprehensive report. Additionally, you are required to submit a portfolio of evidence from practical exercises undertaken during the course.

1.1.        Learning Outcomes

LO1. Demonstrate comprehension of a range of AI techniques and their

application to problem solving within society, industry, and research.

LO2. Articulate awareness of contemporary developments in the field of AI and

their application and potential implications.

LO3. Critically assess real-world problems and determine which AI approaches

are suitable for their solutions.

1.2. Deadlines

Files submitted via Canvas. Deadline Thursday, 09th January 2025

                                                                         13:59 pm

2. Important Information

All work is to be completed individually, except where explicitly stated, and you will only be able to receive Marks for your own work. You are responsible for the security and integrity of your own files, and you must not permit others access to your assignment work. Plagiarism or paraphrasing without due accreditation will be dealt with severely asset out in the University Infringement of Assessment Regulations and detailed in the Programme Handbook. You can also refer to the library guidebook on plagiarism such as Avoiding plagiarism - University Library Services (sunderland.ac.uk)

Students   are   permitted   to   use AI   tools used   in   an   assistive   role   within   the assessment. However, the student must declare in the submission the used tool(s) and

how did you use it.Examples of where AI might be used in an assistive category include:

•     Drafting and structure content.

•     Supporting the writing process in a limited manner.

•     As a support tutor.

•     Supporting a particular process such as translating content.

•     Giving feedback on content or proofreading content

However, students cannot use AI tools to do the project for you as the work must be completely done by the students. All AI generated content  must  be validated  by the student.

You are expected to submit work in the file formats requested. Submitting links to files saved elsewhere in the cloud will not be considered and will result in a zero mark. The actual files must be loaded to Canvas and readily available to the assessor. After uploading and submitting your files, you must check that you can also retrieve and open them. It is your responsibility to ensure files are not corrupted at the time of submission and to report any issues immediately to the help desk, copying in your lecturer and to seek alternative arrangements when required.

3. Tasks

You are required to complete three main tasks; the tasks details can be found below:

1.   Development of Prototype (30%):

Develop  a  prototype  that  relates  to  the  selected  project.  The  prototype  should showcase your practical skills and knowledge in AI. Ensure the prototype is functional and aligns with the project objectives. For details about the prototype please refer to the scenario.

2.   Evaluative Report (40%):

Write an evaluative report that documents your development process, the performance and  functionality  of  the  prototype,  and  the  extent  to  which  it  meets  the  project objectives. Your report should critically assess the strengths and weaknesses of your prototype,  propose  potential  improvements,  and  discuss  the  implications  of  your findings. The report must include a link to the e-portfolio that has your weekly workshop notebook.

3.   Portfolio of Evidence (30%):

Compile a portfolio of evidence from practical exercises completed during the course. This may include code samples, design documents, project notes, or any relevant material that demonstrates your practical engagement with the course material.

4. Deliverables:

1.   Jupyter notebook with the prototype script. (for the used datasets, you can cite it in the report or upload a zip file that contains the Jupyter notebook and any other required files). The notebook should have comments and must show all the results.

2.  A report explaining each step of the development and containing the link to the e portfolio. The report structure is shown in the next section.

3.   E-portfolio link showing your weekly work. The portfolio must be hosted on university hosting service (no external services are acceptable) and must be accessible to the module delivery teams,

5. Report Structure

The evaluative report must include the following sections:

• Cover sheet the cover sheet must be upload filled for the report.

• Abstract (less than 150 words): This should provide a high-level overview of the project, including its goals, objectives, and outcomes.

• Introduction (1-2 pages): This should provide more detailed information about the project, including its background, motivation, and scope.

• Literature Review (1-2 pages): Short literature review of the most relevant research papers on the project.

• Methodology (2-3 pages): This should describe the methods and techniques that were used to complete the project.

• Results and Discussion (2-3 pages): This should present the findings of the project in a clear and concise manner. The results should be interpreted and  discuss their implications.

• References: This should list all the sources that were cited in the report.

6. Scenario

Imagine you are attending a job interview at a charitable organisation and have been asked to prepare a project that demonstrates your skills and professionalism. The focus of the project is to showcase  how  artificial  intelligence  can  be  applied  to  support Alzheimer’s  disease research  or  diagnosis.  For  instance,  you  could  develop  a  machine  learning  model  that classifies individuals as likely or unlikely to have Alzheimer’s. Alternatively, you might predict MMSE (Mini-Mental State Examination) scores using machine learning techniques. You are free to choose your dataset format, whether tabular, image, or audio, as all are acceptable for this project.

7. Marking Criteria

Task 1 - Prototype (30 Marks)