• 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. (70 marks)
Write a 2500 words research report, 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 below to ensure your report includes all required sections.
NOTE: You can choose to complete the coursework using any one of the following approaches:
- Mixture of image processing with artificial neural networks (with Matlab or Python)
- Deep learning only (with Matlab or Python)
You must choose one of the following image datasets for your coursework:
- 7,000 Labeled Pokemon, https://www.kaggle.com/lantian773030/pokemonclassification
- Fruits 360, https://www.kaggle.com/moltean/fruits
- Comic Books Images, https://www.kaggle.com/cenkbircanoglu/comic-books- classification
- Simpsons Main Characters, https://www.kaggle.com/mlwhiz/simpsons-main- characters
- Animals-10 – Animal pictures of 10 different categories taken from google images, https://www.kaggle.com/alessiocorrado99/animals10
- Four Shapes – 16,000 images of four basic shapes (star, circle, square, triangle), https://www.kaggle.com/smeschke/four-shapes#1002.png
- Fruit Recognition – 44406 labelled fruit images, https://www.kaggle.com/chrisfilo/fruit-recognition#Apple%20102.png
- Natural Images – A compiled dataset of 6899 images from 8 distinct classes, https://www.kaggle.com/prasunroy/natural-images#motorbike_0000.jpg
Task 3 (10 marks)
During the timetabled practical session of Week 12 and in groups of 2:
• You will each present the approach you have taken in Task 2 to the other group member, who will question your approach and provide feedback.
• Each presentation should last for 10 minutes.
Submission for Task 1 and Task 2
Prepare the research report and upload to correct Turnitin link before the submission deadline.
The report should include the following sections, the marks for each section are highlighted:
Introduction (5 Marks)
• Objective of the coursework (Research questions(s) you are exploring) (3)
• An overview of the report content (2)
Simulations (40 Marks)
• Provide a description of the dataset, including sample images (10)
• How did you encode the dataset so that you could use the images as input to the neural network? (15)
• Explain the network architecture that you used, how you trained, validated and tested the network, explain the learning algorithm used. (15)
Results Obtained (15 marks)
Describe your results in the three different ways:
- As a percentage (%) accuracy for the test set, i.e. the test set achieved 95% accuracy.
- 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.
- Include a confusion matrix figure as a visual representation of the accuracy you achieve from the test set.
Critical Analysis of results (10 Marks)
o Provide detailed analysis and discussion of the results you achieved.
o Provide information on how you have achieved the results, by making changes to the
Conclusions (10 Marks)
• Restate the research problem addressed in the paper
• Summarize your overall arguments or findings
• Suggest the key takeaways from your paper
MATLAB Certificates (20 Marks)
• Course 1: MATLAB Onramp (5 marks)
• Course 2: Machine Learning Onramp (5 marks)
• Course 3: Deep Learning Onramp (5 marks)
• Course 4: Image Processing Onramp (5 marks)
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