Applications of Deep Learning: Anomaly Detection, Sentiment Classification, and Over-sampling
The talk will present my latest work in deep learning for advancing three strands: I) anomaly detection using stacked denoising auto-encoders. II) Sentiment analysis of variable size datasets using a combined LDA and deep learning approach III) deep over-sampling to overcome class bias in highly imbalance datasets. The methods are demonstrated through real-life problems including: spam filtering, sentiment analysis, and Brain-Computer Interfaces.
Dr. Al Moubayed is an Assistant Professor at the school of computer science in Durham University. Her main research interest is in unsupervised deep learning, natural language processing, and optimisation. Dr. Almoubayed obtained her PhD from the Robert Gordon University, followed by post-doctoral positions in the University of Glasgow and Durham University. She developed machine learning and deep learning solutions in the areas of social signal processing, cyber-security, and Brain-Computer Interfaces. All of which involve high dimensional, noisy and imbalance data challenges.