Brian Wilt discusses how applied machine learning techniques and data science helped Jawbone build a successful fitness tracking app.
Lucian Vlad Lita focuses on the next step in personalization: well-designed software architectures for storing, computing, and delivering responsive, accurate in-product predictions and experiments.
Leah McGuire describes the machine learning platform Salesforce wrote on top of Spark to modularize data cleaning and feature engineering.
Ajit Jaokar discusses data science and IoT: sensor data, real-time processing, cognitive computing, integration of IoT analytics with hardware, IoT’s impact on healthcare, automotive, wearables, etc.
Mini-talks: The Machine Intelligence Landscape: A Venture Capital Perspective. The future of global, trustless transactions on the largest graph: blockchain. Algorithms for Anti-Money Laundering
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.