45:25
Machine Learning Fast and Slow
Suman Deb Roy talks about some of Betaworks’ internal data tools and platform, product-specific solutions and best practices they learned when machine learning has to drive the startup road.
46:38
Vowpal Wabbit, A Machine Learning System
John Langford discusses how to use Vowpal Wabbit in and as a machine learning system including architecture, unique capabilities, and applications, applied to personalized news recommendation.
34:20
Online Data Mining and Machine Learning
Edo Liberty presents some basic concepts and an introduction to the subfields of machine learning and data mining.
41:26
Hunting Criminals with Hybrid Analytics
David Talby demos using Python libraries to build a ML model for fraud detection, scaling it up to billions of events using Spark, and what it took to make the system perform and ready for production.
39:15
How Comcast Uses Data Science and ML to Improve the Customer Experience
Jan Neumann presents how Comcast uses machine learning and big data processing to facilitate search for users, for capacity planning, and predictive caching.
49:35
Startup ML: Bootstrapping a Fraud Detection System
Michael Manapat talks about how to choose, train, and evaluate models, how to bridge the gap between training and production systems, and avoiding pitfalls.
46:06
Artificial Intelligence that Plays Atari Video Games: How Did Deep Mind Do It?
Kristjan Korjus discusses deep learning, reinforcement learning and their combination called deep Q-Network.
56:34
Insights from History of Rock Music via Machine Learning
Ali Kheyrollahi uses clustering and network analysis algorithms to analyze the publicly available Wiki data on rock music to find mathematical relationship between artists, trends and subgenres.
45:03
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.
48:00
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.
50:53
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.
49:07
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.
