Updated: 23/10/2020I am currently adopting MSc students who will be taking my Semester 2 module ECS7005P "Risk assessment for data science and AI". On this module students will learn how to use specialist software to build causal models (Bayesian networks) that enable risk assessment and decision support for a broad class of problems where there are insufficient data for pure machine learning algorithms to provide useful or accurate results. We call this the 'smart data' approach - in contrast to the 'big data' approach.
This 5 minute video explains why smart data, rather than big data, can achieve 'true AI": https://youtu.be/-7vSiWRasxY
For example, imagine you were trying to learn from data whether customers applying for loans are likely to default; the data available is about people previously given loans and whether or not they defaulted- but we have no data about people who were refused loans. Or imagine trying to learn from data whether patients with particular risk factors are likely to suffer from a particular disease; the data available are about previous patients known to have suffered from the disease, but we have no data about people who did not suffer from the disease and no data about the kind of interventions or treatments any of the patients had. In cases such as these we must use knowledge to build an underlying causal model and then a combination of data and knowledge to 'paramaterize' the model.
Each project will use a combination of data and knowledge to develop a Bayesian network model for a working decision support system for one of the following kind of problems (although I am open to other well motivated suggestions):
Two short 2-page overview articles of the smart data approach are:
Previous highly successful projects:
|Norman Fenton website last updated
on 23 Oct, 2020.
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