Claudia Perlich discusses privacy-preserving representations, robust high-dimensional modeling, large-scale automated learning systems, transfer learning, and fraud detection.
Seth Juarez introduces the nuML machine learning library, addressing the clustering issue in .NET applications by focusing on recommendation engines and anomaly detection.
Aish Fenton discusses Netflix' machine learning algorithms, including distributed Neural Networks on AWS GPUs, providing insight into offline experimentation and online AB testing.
Derek Collison discusses some of the technologies and approaches for building a self-healing infrastructure: Intelligent layer 7 SDN with semantic awareness, self healing techniques, etc.
Sean Owen provides examples of operational analytics projects, presenting a reference architecture and algorithm design choices for a successful implementation based on his experience Oryx/Cloudera.
Joseph Wilk addresses the questions if machines can be creative and what's the place of artists in such a world?
Josh Wills discusses using Hadoop technologies to build real-time data analysis models with a focus on strategies for data integration, large-scale machine learning, and experimentation.
Xavier Amatriain discusses the machine learning algorithms and architecture behind Netflix' recommender systems, offline experiments and online A/B testing.
Jenny Finkel showcases Prismatic's use of machine learning and language processing to provide targeted content to their users based on a model built on users' way of interacting with their website.
Andy Gordon discusses machine learning using functional programming, explaining how Infer.NET Fun turns the succinct syntax of F# into an executable modeling language for Bayesian machine learning.
Hilary Mason presents the history of machine learning covering the most significant developments in the area, and showing how bit.ly uses it to discover various statistical information about users.