Assignment Cover Sheets and Data Science Skills Assessment

Assignment cover sheets can be downloaded from UEL Plus via the following pathway.

All work has to be presented in a ready to submit state upon arrival at the ACE Helpdesk. Assignment cover sheets or stationery (including staplers) will NOT be provided by Helpdesk staff. This will mean students will not be able to staple cover sheets at the Helpdesk.

Task 1: Data Science Skills (20 marks)

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. (5 marks)
• Course 2: Machine Learning Onramp - Learn the basics of practical machine learning methods for classification problems. (5 marks)
• Course 3: Deep Learning Onramp - Get started quickly using deep learning methods to perform image recognition. (5 marks)
• Course 4: Image Processing Onramp - Learn the basics of practical image processing techniques in MATLAB. (5 marks)

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.

NOTE 1: 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)

Introduction (5 Marks)

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

o An overview of the report content

Methodology (10 Marks)

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

Simulations (30 Marks)

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 (10 marks)

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.

As in this heading, they should be 12 Point Times New Roman boldface, initially capitalised, flush left, with one blank line before, and one after.

Third-order headings. Thirdorder headings, as in this paragraph, are discouraged. However, if you must use them, use 12 Points Times New Roman boldface, boldface, initially capitalised, flush left, preceded by one blank line, followed by a period and your text on the same line.