Machine learning is the branch of artificial intelligence giving computers the ability to learn patterns from data without being explicitly programmed. Deep Learning is a set of cutting-edge machine learning algorithms that are inspired by how the human brain works. It allows to selflearn feature hierarchies from the data rather than modeling hand-crafted features. It has proven to significantly improve performance in challenging data analytics problems.

• An introduction to the theoretical foundations of neural networks and Deep Learning.
• A demonstration of how to use Deep Learning for character recognition in a cloud using a distributed environment for Big Data analytics
• This combines Apache Spark and TensorFlow, Google’s in-house Deep Learning platform made for Big Data machine learning applications.

Patrick Glauner is a Ph.D. Student at the University of Luxembourg working on the detection of electricity theft in emerging markets through Machine Learning. His research was featured in New Scientist and cited in the McKinsey Global Institute discussion paper “Artificial intelligence: The next digital frontier?”. He also holds two adjunct faculty appointments at the Universities of Applied Sciences in Karlsruhe and Trier. In parallel, he is pursuing an MBA with Smartly. He graduated as valedictorian from Karlsruhe University of Applied Sciences with a BSc in Computer Science and obtained his MSc in Machine Learning from Imperial College London. He was a CERN Fellow, worked at SAP and is an alumnus of the German National Academic Foundation (Studienstiftung des deutschen Volkes). His current interests include anomaly detection, computer vision, deep learning, natural language processing, power supply, smart grid and time series.