The purpose of this session is to demystify the central ideas behind pattern recognition and machine learning by demonstrating two key elements of the same: namely classification and clustering. Most developers shy away from such algorithms simply because of their perceived difficulty while missing the inherent simplicity of these approaches. The first part of the session will demonstrate how a computer can learn from labeled examples in order to predict appropriate labels for future examples. The second portion will deal with learning the structure of data without having to know anything about it a priori.

Seth Juarez has a Master’s Degree in Computer Science where his field of research was Artificial Intelligence specifically in the realm of Machine Learning. He is a Technical Evangelist for DevExpress where he specializes in data analysis and shaping in conjunction with their reporting toolset. When he is not working in that area, he devotes his time to an open source Machine Learning Library specifically for .NET that is intended to simplify the use of popular supervised and unsupervised learning models.