InfoQ Homepage Machine Learning Content on InfoQ
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NET Machine Learning: F# and Accord.NET
Alena Hall presents various machine learning algorithms available in Accord.NET - a framework for machine learning and scientific computing in .NET.
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Dino DNA! Health Identity from the Wrist @Jawbone
Brian Wilt discusses how applied machine learning techniques and data science helped Jawbone build a successful fitness tracking app.
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Takes a Village to Raise a Machine Learning Model
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.
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The Lego Model for Machine Learning Pipelines
Leah McGuire describes the machine learning platform Salesforce wrote on top of Spark to modularize data cleaning and feature engineering.
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Machine Learning and IoT
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.
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Mini-talks: Machine Intelligence, Algorithms for Anti-Money Laundering, Blockchain
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
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The Deep Learning Revolution: Rethinking Machine Learning Pipelines
Soumith Chintala introduces deep learning, what it is, why it has become popular, and how it can be fitted into existing machine learning solutions.
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Become a Data-driven Organization with Machine Learning
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.
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Machine Learning for Programming
Peter Norvig keynotes on using machine learning techniques to solve more general software problems, helping both the advanced programmer and the novice one.
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My Three Ex’s: A Data Science Approach for Applied Machine Learning
Daniel Tunkelang focuses on the data science mindset for successfully applying machine learning to solve problems: express, explain, experiment.
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Weathering the Data Storm
Claudia Perlich discusses privacy-preserving representations, robust high-dimensional modeling, large-scale automated learning systems, transfer learning, and fraud detection.
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Practical Machine Learning
Seth Juarez introduces the nuML machine learning library, addressing the clustering issue in .NET applications by focusing on recommendation engines and anomaly detection.
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