Research Centre for Machine Learning meeting on Explainable AI
When: Fri, 29 November 2019, 4:00pm
Where: AG01, College Building
SHAP is an increasingly popular method for providing local explanations of AI system predictions. SHAP is based on the game-theory concept of Shapley Values. Shapley Values are the unique solution for fairly attributing the benefits of a cooperative game between players, when subject to a set of local accuracy and consistency constraints (an excellent introduction to Shapley Values is provided at https://www.youtube.com/watch?v=qcLZMYPdpH4&t=437s)
We will be discussing Lundeberg and Lee’s paper ‘A Unified Approach to Interpreting Model Predictions’ (2017) in which they propose SHAP and claim that it unifies six explainable AI methods. The aim of the meeting will be both to gain a better understanding of SHAP and to evaluate its usefulness. Dr Adam White will begin the meeting by providing a critical overview of SHAP.
As always – all welcome!
Data Bites seminar
When: Mon, 11 November 2019, 5:00pm
Where: A130, College Building
Who: Kevin Ryan; City, University of London
Title: Deep Learning and Computer Vision in the Property Market – Making the ‘Right’ Move
Abstract: Rightmove is the UK’s largest online real estate portal. The company was started in 2000 by the top four corporate estate agents Countrywide, Connells, Halifax and Royal and Sun Alliance. In 2006 it was floated on the London Stock Exchange and today its boasts a revenue of £267m with an operating profit of £198.6m.
Rightmove offers an Automated Valuation Model (AVM) which predicts the price of a UK-based property based principally on easy to measure property metrics such as number of bedrooms, previous sold price, asking price, location etc. These metrics are generalisable across different property types and are effective in capturing gross differences in price. However, they do not capture more specific differences in the marketability between properties such as the presence/absence of specific features or style/decor-based characteristics that can often play a significant effect on sold price.
Property images contain a great deal of unstructured information relating to these more nuanced features of a property. In this talk I will discuss some of the data gathering and deep learning approaches that I used in order to capture marketable information from property images. I will also discuss a little background around how I sourced and obtained my internship at Rightmove as part of my MSc in Data Science.
Bio: Dr Kevin Ryan is currently completing City’s MSc in Data Science. Previously he worked as one of the principal Bioinformaticians at Viapath where he was responsible for implementing an end-to-end analysis platform for the High Throughput DNA sequencing facility at Guy’s Hospital’s Genetics Department. His platform went live in 2015 and formed a central part of the service responsible for serving 3.8 million people in the South Thames area.
Prior to this Dr Ryan was based at the University of Nottingham where he worked as a Postdoctoral research scientist. Here his research involved the development of analysis systems to characterise gene expression networks involved in the regulation of skeletal muscle growth and energy metabolism. Originally trained within the fields of molecular biology and nutritional biochemistry, he completed his PhD in 2005 at the University of Nottingham.
He is currently completing an internship at the Property Portal company Rightmove Plc where his project explores the use of Computer Vision approaches in extracting unstructured data from property images to help inform future property price prediction models.