CETM26 - Hyper-parameter Optimisation In created a feedforward artificial neural network (ANN) to solve a binary classification task on tabular, numeric data.

Assignment Task

Task 

Hyper-parameter Optimisation In created a feedforward artificial neural network (ANN) to solve a binary classification task on tabular, numeric data. In this assignment we will expand upon these concepts, and solve a classification task on a harder problem involving image input.

In this assignment, 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 network, various architecture choices, hyper-parameter optimisation 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.