BEGIN:VCALENDAR
PRODID;X-RICAL-TZSOURCE=TZINFO:-//Calagator//EN
CALSCALE:GREGORIAN
X-WR-CALNAME:Calagator
METHOD:PUBLISH
VERSION:2.0
BEGIN:VTIMEZONE
TZID;X-RICAL-TZSOURCE=TZINFO:America/Los_Angeles
BEGIN:DAYLIGHT
DTSTART:20150308T020000
RDATE:20150308T020000
TZOFFSETFROM:-0800
TZOFFSETTO:-0700
TZNAME:PDT
END:DAYLIGHT
END:VTIMEZONE
BEGIN:VEVENT
CREATED;VALUE=DATE-TIME:20150723T143335Z
DTEND;TZID=America/Los_Angeles;VALUE=DATE-TIME:20150817T200000
DTSTART;TZID=America/Los_Angeles;VALUE=DATE-TIME:20150817T180000
DTSTAMP;VALUE=DATE-TIME:20150723T143335Z
LAST-MODIFIED;VALUE=DATE-TIME:20150723T143335Z
UID:http://calagator.org/events/1250468820
DESCRIPTION:Abstract:&#13\;\n&#13\;\nThe discussion will begin with a bri
 ef overview of the current machine learning landscape in R. After the in
 troduction\, we will discuss H2O\, a scalable open source machine learni
 ng library. H2O has APIs in R\, Python\, Scala and Java\, and the focus 
 of this talk will be the `h2o` and `h2oEnsemble` R packages. All of H2O'
 s algorithm implementations are distributed\, which allows the software 
 to scale to big data. H2O can be used to speed up machine learning probl
 ems on your laptop (as a local multicore cluster)\, or it can be used in
  a multi-node cluster setting (for example\, on Amazon EC2). H2O current
 ly features distributed implementations of GLM\, GBM\, Random Forest and
  Deep Neural Nets. H2O.ai\, the company behind H2O\, is based in Mountai
 n View\, CA and has a scientific advisory council comprised of very well
  known contributors to machine learning community: Trevor Hastie\, Rob T
 ibshirani and Stephen Boyd\, all from Stanford University.&#13\;\n&#13\;
 \n&#13\;\nSpeaker Bio:&#13\;\n&#13\;\nErin is a Statistician and Machine
  Learning Scientist at H2O.ai\, and the author of several R packages. Er
 in received her Ph.D. in Biostatistics with a Designated Emphasis in Com
 putational Science and Engineering from University of California\, Berke
 ley. Her research focuses on ensemble machine learning\, learning from i
 mbalanced binary-outcome data\, influence curve based variance estimatio
 n and statistical computing. http://www.stat.berkeley.edu/~ledell http:/
 /www.twitter.com/ledell\n\nTags: R machine learning software\, data scie
 nce\n\nImported from: http://calagator.org/events/1250468820
URL:http://www.meetup.com/portland-r-user-group/events/224100404/
SUMMARY:Scalable Machine Learning in R with H2O
LOCATION:Simple: 1615 SE 3rd Ave\, Suite 200\, Portland OR 97214 US
SEQUENCE:2
END:VEVENT
END:VCALENDAR