You will be expected to research techniques of your own accord, potentially beyond those that are taught within the module. This may involve state-of-the-art techniques in machine learning. You are expected to come up with a methodical and scientific approach to creating a model for solving multi-class classification of images, incorporating many of the techniques within the module such as different types of networks, various architecture choices, hyper-parameter optimization techniques, dimensionality reduction methods, etc. All of this will be written into a report outlining your project, comparing results, and evaluating your experiments. At the end of this report you will conclude your findings, and discuss considerations for these technologies towards the theme of ethical use of AI and how these systems may be both beneficial and/or disruptive; specifically in the context of the proposed solution for the CIFAR-10 dataset challenge and its potential applications. Dataset The dataset for this assignment is the CIFAR-10 dataset. The CIFAR-10 dataset consists of 60000 32×32 colour images in 10 classes, with 6000 images per class. There are 50000 training images and 10000 test images. Link download dataset: https://www.cs.toronto.edu/~kriz/cifar.html Maximum of 3500 words. You will need to submit any and all Python code for this section. It will not carry marks itself, but will be checked to ensure it supports the functionality presented within the report, and for plagiarism purposes. Your report should be written in the form of a research project, for the task of multi-class classification of the CIFAR-10 dataset. You should consider all the techniques and tools discussed in the module so far. You do not have to use each and every technique. You may also additionally look into further techniques from literature to expand your experiments and comparisons. Skills: Python, Machine Learning (ML), Research Writing
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