Machine learning for big data analytics

Abstract

During the past 30 years, the amount of stored digital data has roughly doubled every 40 months. Today, about 2.5 quintillion bytes are created very day. This data comes from sensor networks, cameras, microphones, mobile devices, software logs etc. Part of it is scientific data especially in particle physics, astronomy and genomics, part of it comes from other sectors of society such as internet text and documents, web logs, medical records, military surveillance, photo and video archives and e-commerce. This data poses a unique challenge in data mining: finding meaningful things out of the data masses. Central algorithmic techniques to process and mine the data are classification, clustering, neural networks, pattern recognition, regression, visualization etc. Many of these fall under the term machine learning. In the author's research group at Aalto University, Finland, machine learning techniques are developed and applied to many of the above problems together with other research institutes and industry. The talk will cover some recent algorithmic discoveries and illustrate the problem area with case studies in speech recognition and synthesis, video recognition, brain imaging, and large-scale climate research. © Springer-Verlag Berlin Heidelberg 2013.