Nov 3 (NYC Predictive Analytics) Hidden Markov Models in a Nutshell
Description: Hidden Markov Models (HMMs) have emerged as a powerful paradigm for modeling stochastic processes and pattern sequences. Originally, HMMs have been applied to the domain of speech recognition, and became the dominating technology. In recent years, they have attracted growing interest in automatic target detection and classification, computational molecular biology, bioinformatics, computational finance, mine detection, handwritten character/word recognition, and other computer vision applications. The purpose of this talk is to define HMM and its categories, present the corresponding underlying problems, and explain the step-by-step working of the most popular procedure for HMM parameter estimation: Baum-Welch algorithm.
This group is one of my favorite groups to go to, and any time there is a talk on Markov I am there.
Nov 8 ( NYC R Meetup) Parallel R with Hadoop
Nov 10 (Greater Boston R users) Teaching Statistics with Open Source Tools
Nov 14 (DC R User Group) Moneyball Meets R: Sabermetrics with the MLB Pitch Data Set by Mike Driscoll
7:00 - 7:30: Enterprise Case Studies: Rob Lancaster and Patrick Angeles of Cloudera, a company which provides enterprise solutions that extends upon Hadoop functionality, will be presenting a high-level overview of big data and associated applications. Secondly, they will be presenting a variety of "use cases" including diving into technical details of Hadoop and related software.
7:30 - 8:00: "Open Data" Project: Satish Gopalakrishnan and Vineet Manohar will be presenting their Wikipedia / Hadoop project which they created as part of the Hack/Reduce event this past summer at Microsoft NERD. Their computer program was voted the coolest hack using Hadoop with open data.