MPhil-PhD transfer presentation
When: Fri, 1st Mar 2019, 2.00pm
Where: C323 (3rd Floor, Tait Building)
Who: Charitos Charitou; City, University of London
Title: Deep Learning for Compliance: “Application of machine learning to online gambling data to identify money laundering”
Abstract: Most of the current online gambling operators are using handcrafted basic rules for their anti money laundering (AML) strategy. These methods are not enough anymore for identifying complex fraudulent activities. Kindred group entered into research collaboration with City University and the main goal is to effectively use machine learning to detect money laundering. Understanding the needs of the industry and what the industry stakeholders believe was a priority. A series of interviews with various stakeholders of the gambling industry took place and the findings were published earlier this year in the form of a white paper.
The second part of the research involved the analysis and evaluation of the gambling data that were provided by Kindred. We present how the imbalanced dataset problem was tackled, and the new experimental dataset that was created for supervised learning. The performance of Logistic Regression (LR), Random Forest (RF) and Multilayer perceptron (MLP) was examined and compared. Our results, showed that Random Forest was the best model for predicting the normal players, while the MLP managed to detect suspicious players with the higher accuracy. Finally, the sequential relationship of the data was investigated using discrete and continuous Hidden Markov Models (HMM).
Machine Learning seminar
When: Wed, 20 Feb 2019, 2pm
Where: AG21, College Building
Who: Dr. Alberto Ferreira De Souza, Universidade Federal do Espírito Santo (UFES), Brazil.
Title: Building IARA – The Intelligent Autonomous Robotic Automobile
The Intelligent Autonomous Robotic Automobile (IARA) is one of the most advanced self-driving cars in world, figuring in eighth place according to the metric of number of interventions per 1000 miles in 2017. We start building IARA in 2009 and, since then, more than 30 students, including Ph.D., M.Sc., and undergraduates, have completed their courses working on the project. IARA is based on the precise localization paradigm where the self-driving car must have a detailed map of the environment to operate autonomously. In this talk, we will present some of the history behind the 10 years of research that led to the current status of development of IARA and will describe how some of its main software modules work, including the modules responsible for mapping, localization and autonomous navigation.
A demonstration video can be found here.
Dr. Alberto Ferreira De Souza is a Professor of Computer Science and Coordinator of the Laboratório de Computação de Alto Desempenho (LCAD – High Performance Computing Laboratory) at the Universidade Federal do Espírito Santo (UFES), Brazil. He received B. Eng. (Cum Laude) in electronics engineering and M. Sc. in systems engineering and computer science from Universidade Federal do Rio de Janeiro (COPPE/UFRJ), Brazil, in 1988 and 1993, respectively; and Doctor of Philosophy (Ph.D.) in computer science from the University College London, United Kingdom in 1999. He has authored/co-authored one USA patent and over 130 publications. He has edited proceedings of four conferences (two IEEE sponsored conferences), and is a Standing Member of the Steering Committee of the International Conference in Computer Architecture and High Performance Computing (SBAC-PAD).