Acquire Data Science Skills and Implement Neural Network Classification for Image Datasets

Task 1: Data Science Skills

Complete four MATLAB Online Courses to learn Data Science skills. Earn a certificate for each course, acquire four certificates to complete this part of the assessment. When completed, upload each certificate to Turnitin.

• Course 1: MATLAB Onramp - Get started quickly with the basics of MATLAB.
• Course 2: Machine Learning Onramp - Learn the basics of practical machine learning methods for classification problems.
• Course 3: Deep Learning Onramp - Get started quickly using deep learning methods to perform image recognition.
• Course 4: Image Processing Onramp - Learn the basics of practical image processing techniques in MATLAB.

Task 2: Design, implement and report on neural network-based techniques for classification of a dataset of images. (80 marks) Write a 3000 words research report, in the style of a research paper, including the following:

• The research question(s) you are exploring and the experiments you designed to address these question(s)
• A clear presentation of the methods (neural network implementation, network architectures, training regime, etc.) that were used, an outline of how they were implemented, and a discussion of why these methods were chosen.
• A clear presentation of results, discussion and interpretation of results and conclusions.
• Please follow the marking scheme to ensure your report includes all required sections. You can choose to complete the coursework using any one of the following approaches:

1. Mixture of image processing with artificial neural networks (with Matlab or Python)
2. Deep learning only (with Matlab or Python)

Submission

Task 1 – Upload each MATLAB certificate to the correct Turnitin link before the submission

deadline.

Task 2 - Prepare the 3000 words research report and upload to correct Turnitin link before the submission deadline.

o Maximum number of words:

Introduction

o Objective of the coursework (Research questions(s) you are exploring)

o An overview of the report content

Methodology

o Discuss neural network classification for image datasets. This should include references from at least 5 conference papers Simulations

o Provide a description of the dataset, including sample images

o How did you encode the dataset so that you could use the images as input to the neural network.

o Explain the network architecture that you used, how you trained, validated and tested the network, explain the learning algorithm used.

Results Obtained

Describe your results in the three different ways:

1. As a percentage (%), i.e. the test set achieved 95% accuracy.
2. Include an accuracy curve figure for the training, testing and validation results. The x-axis will represent the number of epochs and the y-axis will represent the percentage accuracy.
3. Include a confusion matrix figure as a visual representation of the accuracy you achieve.