SF Machine Learning Meetup March 2015
Friday, March 6, 2015
SF Machine Learning Meetup group focuses in understanding and discussing differences of various implementations of modern machine learning algorithms like Support Vector Machine, Random Forest and Neural Nets, which are producing very promising results on large scale datasets.


What makes machine learning easy to program and what makes it fast?

Ted Dunning View Bio

The new Mahout DSL has two aims. One, to make it easy to program distributed machine learning algorithms using a math-like notation for the programs. The secondary goal is to allow such programs to be fairly performant by allowing alternative back-end computational engines. The primary back-end for Mahout is currently Spark, but there is also work going on with the h2o system. I will talk about how these back-ends help achieve these two goals, with particular attention to how speed is achieved.


Ted Dunning

Ted Dunning is Chief Application Architect at MapR Technologies and committer and PMC member of the Apache Mahout, Apache ZooKeeper, and Apache Drill projects​. Ted has been very active in mentoring new Apache projects and is currently serving as vice president of incubation for the Apache Software Foundation​.​ Ted was the chief architect behind the MusicMatch (now Yahoo Music) and Veoh recommendation systems. He built fraud detection systems for ID Analytics (later purchased by LifeLock) and he has 24 patents issued to date and a dozen pending. Ted has a PhD in computing science from the University of Sheffield. When he’s not doing data science, he plays guitar and mandolin. He also bought the beer at the first Hadoop user group meeting..