Machine Learning

Machine learning is a subset of artificial intelligence in the field of computer science that often uses statistical techniques to give computers the ability to "learn" (i.e., progressively improve performance on a specific task) with data, without being explicitly programmed.

The name machine learning was coined in 1959 by Arthur Samuel. Evolved from the study of pattern recognition and computational learning theory in artificial intelligence, machine learning explores the study and construction of algorithms that can learn from and make predictions on data – such algorithms overcome following strictly static program instructions by making data-driven predictions or decisions, through building a model from sample inputs. Machine learning is employed in a range of computing tasks where designing and programming explicit algorithms with good performance is difficult or infeasible; example applications include email filtering, detection of network intruders or malicious insiders working towards a data breach, optical character recognition (OCR), learning to rank, and computer vision.


Position on the Adoption Curve

Presentations about Machine Learning

Java and Neural Network Expert, Creator @Neuroph, & Founder @DeepNetts Zoran Sevarac

Deep Learning for Application Performance Optimization

Research engineer @Cloudera Fast Forward Labs Mike Lee Williams

Probabilistic Programming from Scratch

Agile & Software Craftsmanship Coach Harold Shinsato

Machine Learning Open Space

Senior Software Engineer, Operational Insights @Netflix Seth Katz

How Machines Help Humans Root Cause Issues @Netflix

Lead Data Architect, Risk and Compliance Management Platform @PayPal Mikhail Kourjanski

ML Data Pipelines for Real-Time Fraud Prevention @PayPal

Language Designer Working on ML + Tooling @Facebook & previously Developed TypeScript, F#, & Swift Joe Pamer

Software Is Eating the World, ML Is Going to Eat Software


See more interviews