Soumith Chintala introduces deep learning, what it is, why it has become popular, and how it can be fitted into existing machine learning solutions.
Peter Harrington explains what you do with machine learning, and what are the building blocks for an application that uses machine learning from collected data to creating predictions for customers.
Peter Norvig keynotes on using machine learning techniques to solve more general software problems, helping both the advanced programmer and the novice one.
Daniel Tunkelang focuses on the data science mindset for successfully applying machine learning to solve problems: express, explain, experiment.
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.