APPLIED DEEP LEARNING IN FINANCE

Over the last couple of years, we have seen a resurgence of interest in Machine Learning applications by researchers and practitioners alike. The most considerable breakthroughs have occurred in the subfield of Deep Learning – a collection of Machine Learning methods inspired by the workings of the human brain. Today, Deep Learning is at the heart of many state-of-the-art (predictive) analysis tools providing a competitive edge to organizations, especially in the financial industry. Examples include credit default prediction, pattern recognition, stock price prediction, sentiment analyses, outlier detection, and natural language processing, to name only a few. The course introduces Deep Learning methods, including Feed Forward Neural Networks, Convolutional Neural Networks, and Recurrent Neural Networks. Learned methods will be applied to problems such as credit default, customer churn, and stock price predictions. Against the background of recent debates and regulations concerning the “black box” nature of Deep Learning applications, the course will also touch upon interpretability methods.