Reference no: EM132888834

KF7006 Machine Learning - Northumbria University

Assignment - The Design, Development, Analysis and Performance Evaluation of Deep Learning algorithms

Learning Outcome 1: Demonstrate knowledge and understanding of the core concepts of machine learning and its underlying mathematical foundations
Learning Outcome 2: Demonstrate knowledge and understanding of the principal advanced machine learning techniques for solving real world problems.

Intellectual / Professional skills & abilities:

• Critically evaluate machine learning algorithms and applications.
• Analyse, design and develop machine learning solutions and evaluate their performance

Nature of the submission required:
All the work for this assessment should be produced as a word (.docx) or PDF document (.pdf) for the report plus a single zipped file (.zip) of the code. This report will be then submitted to Turnitin and the code will be submitted directly to Blackboard. Both of the report and code should identify the student by name and ID.
Instructions to students:
This is an individual work and you CANNOT work with others to construct your work. During the semester there are numerous opportunities to seek and get advice and support on your work, from tutors and peers but you must ensure you do not do work for others or copy work from others.

Academic Conduct:
You must adhere to Northumbria University regulations on academic conduct. Assessment Regulations for Taught Awards (ARTA) contain the regulations and procedures applying to cheating, plagiarism and other forms of academic misconduct. The full policy is available on the University website. You are reminded that plagiarism, collusion and other forms of academic misconduct as referred to in the Academic Misconduct procedure of the assessment regulations are taken very seriously. Assignments in which evidence of plagiarism or other forms of academic misconduct is found may receive a mark of zero.
If you need an extension:
Contact ask4Help. Tutors and Module tutors cannot change deadlines.

Disabled students
Contact the module lead tutor about reasonable adjustments.

Submission Requirements

You must comply to the following criteria to fulfil the assignment submission requirements:

o The word limit is 2000. However, if the assignment is within +10% (i.e., up to 200 words) then NO penalty will be applied.
o The word count should be declared on the cover page of your assignment. The word count does not include title page, table of contents page, references and appendices. Please note, in text citations [e.g. (O'Brien, 2020)].

Assessment Brief

Lately, deep Learning has a tremendous amount of attention especially in medical image analysis. In this assignment you will be required to design, develop, analyse and evaluate an appropriate deep learning model. You can build your own model or use a pretrained model with your layers added to it. You will explore the dataset and then apply that model to a dataset of your choosing. You will need to evaluate the performance in terms of precision, recall, F1-score, ROC-curve and PR-curve. You will discuss the findings that have been produced, and critically reflect upon the model and its predictions.

Assessment Tasks:

You have been provided with access to three datasets; all are available on Kaggle (Please see links below). The data covers the following scenarios:
• Classification of blood cell types

• Chest X-ray classification to COVID-19, Viral Pneumonia, normal

• Brain tumour detection from MRI images

You are required to choose one of the above scenarios as your assignment. Your task is to produce a deep learning model that is appropriate to the problem. The model can be your own model or designed based on fine-tuning of a pretrained model. You are required to conduct data preparation/transformation to make the data ready for the model. Please note that what will be provided in the report should reflect on the python code. Please also note NOT to take on any existing code online as your own work. The errors in the code will affect your mark final mark.

The key components you must complete are:

1. Explore the dataset to understand its characteristics
2. Pre-process your data to be suitable for building the model
3. Build the model that allows for the task specified for chosen dataset
4. Evaluate the model predictions using the metrics stated above.
5. Fine-tune the model to get better predictions on the test set
6. Present your findings with suitable visualisations that are easy to interpret
7. Critically evaluate and discuss the whole process and he findings and what can be improved

Attachment:- Machine Learning.rar