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single research project in an area of applied AI (written up in the form of a 3,000 word report)

INSTRUCTIONS TO CANDIDATES
ANSWER ALL QUESTIONS

Description:

The assessment for this module will consist of two components: (a) a single research project in an area of applied AI (written up in the form of a 3,000 word report) and (b) a short 1-minute recorded presentation of the project. Topics for the research project will be based on one of the areas we will cover this semester, including feedforward neural networks, natural language processing, or computer vision. Specifically, the idea is to choose one of the areas below and extend a lab session into a full project + report.

Please note - you may not be taught all aspects relating to your topic explicitly in class. It is expected that you will do further reading and research and find out what you need in terms of theoretical background or code base.

Topics are as follows:

Feedforward neural nets with hyperparameter optimisation: this project will implement a feedforward neural network for a dataset of choice (but the data should be different from the lab), and experiment systematically with a number of hyperparameter configurations,

e.g. the learning rate, batch size, number of hidden units, layers, etc. The project will need to explore systematic approaches for hyperparameter optimisation such as random or grid optimisation or genetic algorithms. Note that the specifics will not be taught directly, you’ll need to research and find out how to implement these in Python yourself. You may use any software libraries available, as long as referenced. The approaches named above e.g. come with sk_learn and do not need to be implemented from scratch. The neural network’s performance should be evaluated in different settings and compared against other approaches, such as decision trees, Naive Bayes or other classifiers. Results should be supported with visualisations, such as graphs.

APPLIED AI ASSIGNMENT

 • Text classification: this project will implement a deep learning system for text classification (e.g. using the news dataset from the lab, or any other you can find). You can choose what classes you want to learn (i.e. classify) from your dataset. You will need to make an informed choice of neural network (such as recurrent or transformer) and implement it using a deep learning library. This part can be based on a lab session we did together. The project should also include at least one additional component, e.g. a specific hypothesis you want to investigate, a comparison against another technique, or a data augmentation technique, such as language modelling (i.e. embed features into vector space using one or more techniques e.g. Word2Vec, GloVe, BERT, GPT-2, etc.). Regardless of what you choose to do concretely, make sure that you use baselines in your project, i.e. choose the system you want to “pitch” and make sure you compare it another setup. Results should be supported with visualisations, such as graphs.

Sentiment analysis from text and/or images: this project will implement a deep learning- based system for sentiment analysis. You will need to choose a dataset (e.g. not the one used in our sentiment analysis lab please) and make an informed choice of architecture. Then implement it using a deep learning library. You can then either focus on sentiment analysis from text (as we’ve done before) or image analysis, e.g. predicting sentiment from images. In either case, please make sure to benchmark your results against an alternative setting, e.g. experimenting with more than one neural network architecture, or experimenting substantially with your chosen architecture itself, e.g. using hyperparameter optimisation. Results should be supported with visualisations, such as graphs.

 

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