The field of Machine Learning grew out of the quest for artificial intelligence. It gives computers the ability to learn statistical patterns from data without being explicitly programmed. These patterns can then be applied to new data in order to make predictions. Machine Learning also allows to automatically adapt to changes in the data without amending the underlying model.

• How we deal every day dozens of times with Machine Learning applications such as when doing a Google search, using spam filters, face detection, speaking to voice recognition software or when sitting in a self-driving car
• Why IT professionals cannot afford anymore not to look into Machine Learning when they think and plan for the future.
• An overview of the field of Machine Learning and identify a number of business perspectives

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.