Deep Representation Learning for Disparate Data
Deep leaning based Artificial Intelligence models are showing state-of-the-art results for complex problems in banking and insurance. This talk will focus on learning representations from disparate data, like free-form text and structured categorical and numeric data, in insurance. It will show how a deep network can be trained that represents the knowledge that is required to perform the task at hand and generalises well to novel unseen cases.
Huma is a principal data scientist at Direct Line Group. She has over 15 years of experience in Artificial Intelligence & Machine Learning across both industry and academia. She is an accomplished expert with hands on experience in development and application of Deep Learning, Kernel Methods, Relational Learning and Ensemble Methods for areas ranging from insurance to health care. She has a PhD in Machine Learning from university of London. She is a co-editor of two books and has published many research articles in leading AI & Machine Learning journals and conferences. She regularly speaks at industry events and is keen to transform industries by the use of Artificial Intelligence and Machine Learning.