Machine Learning and Data Modelling - Healthy Control (HC) - Mild Cognitive Impairment (MCI) - IT/Computer Science Assignment Help

Assignment Task:

An individual research project will be conducted on a research topic relevant to machine learning and data modelling. Self-learning via online lectures and literature review will be encouraged. The project exercise can provide students with the background knowledge and practical skills of research methods for their final MSc project development.
The areas under consideration can relate to for example web analytics, government, logistics, telecommunications, social media, security, open data and healthcare. The students will then present their findings of the use and value of machine learning and data modelling approaches, highlighting the strengths and risks associated with each. 
The research project will be assessed through both an oral (30%) and a written (70%) format. The oral assessment consisting of a short 10-minute pitch (8-minute presentation/demo + 2-minute Q&A) will be conducted in a research seminar model during a class in the last week of the semester. The written assessment, together with any relevant codes and data, will be submitted electronically including a 4-page report in the format of IEEE journal paper (template will be provided). 
Students will be given formative verbal feedback as they develop their skills building up to this assignment. 
Students will be provided with both formative and qualitative written feedback after submission within the required timeframe, not exceeding three weeks of the submission deadline. 

This item of coursework will contribute to 50% of the overall module mark. You are required to propose, analyze, design, develop, test, and deliver a research project.
Your developed project must demonstrate your learning to date and your knowledge of machine learning and data modelling, to include the use of at least 3 supervised or/and unsupervised learning algorithms, based on the Australian Imaging Biomarkers and Lifestyle flagship study of ageing (AIBL) data. The data can be applied. The data description can be found by using the Data Dictionary search box. The AIBL data were categorized into three clinical diagnostic results: Healthy Control (HC), Mild Cognitive Impairment (MCI), and Alzheimer’s Disease (AD). This coursework will consider two categories, i.e. HC and Non-HC (combining MCI and AD).
Performance of the machine learning algorithms implemented should be evaluated using an appropriate metric. Finally, the strengths and limitations of the algorithms involved need to be discussed and concluded, following by any potential extensions you may work in future.
Self-learning via online lectures and literature review will be encouraged. This is an opportunity to revisit all topics covered to date in COM737. It will especially help you build the background knowledge and practical skills of research methods and project management for your final MSc project development. Therefore, you are required to devise a detailed project plan and apply a risk management process to your project along the following lines:
Work packages
You will use Work Breakdown Structures to break down your project into lower levels of detail to reveal exactly what work you will need to do to complete your project. 
Milestones and deliverables
These should well identify what will be used to monitor your project progress and what you intend to submit at the end of your project.
Project plan
Your project should be divided into a number of manageable tasks. Each task should be clearly defined and concisely described. A project plan should be presented using a Gantt chart on which tasks, milestones and deliverables are clearly placed. 
Time management 
You should carry out a fundamental analysis about what you want to do and what you are currently doing, and discuss how you will make efficient use of your time during the course of your project.
Risk management 
This should include identifying risks, assessing the impact of risks, alleviating critical risks and controlling risks.

The research project will be assessed through both an oral (30%) and a written format (70%). The oral presentation should present a machine learning process, details of the machine learning algorithm used, and the above research project development process, within 10 mins (including Q&A). The written part of the coursework, together with any relevant and functioning codes and data used, is required to be submitted electronically including an element in the format of an appropriate scientific journal or conference paper, which at least includes the following sections: abstract, keywords, introduction, related work, methods and materials, experimental results, discussion, conclusions, acknowledgement, and references.

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