Machine Learning From Disaster


Details
What: Since our last machine learning meetup was such a success, we've decided to keep the good times rolling and this time we're bringing in speaker Philip Trelford to take us on another hands on machine learning exercise. All levels of experience welcome, from beginner to expert.
For this meetup the goal is to use F# and sample data from Kaggle (http://kaggle.com/) to predict who lives and who dies on the Titanic!
To get the most from the session please try and bring a laptop along with F# installed.
• Install F# on Windows (http://fsharp.org/use/windows/)
• Install F# on OSX (http://fsharp.org/use/mac/)
• Install F# on Linux (http://fsharp.org/use/linux/)
Who: Phil Trelford is a Software Architect at an ISV supplying real-time electronic trading software. His career so far spans over 15 years, with experience in video games, leisure, retail and financial sectors.
Phil’s recent commercial development work has been with C++, C#, SQL, JavaScript, and includes over 2 years developing F# applications at Microsoft.
Phil is also a founding member of the F# Software Foundation (http://fsharp.org/foundation.html), whose mission is to promote, protect, and advance the F# programming language, and to support and facilitate the growth of a diverse and international community of F# programmers.
Where: B-Line Medical is located 2 blocks south of the Dupont Circle metro stop (red line). Exit on dupont south and the building is at the corner of 19th and N street NW. Driving is not encouraged as parking will be extremely difficult. The building needs a key card to get in after 6pm but Anton will be hanging around outside to let people in. B-Line medical is on the first floor of the building right around the corner at the end of the hallway.
The space was a bit cramped last time so I've set the RSVP limit to 25 people, though depending on RSVP count I may increase this if people are ok with not having a table and/or finding a random seat to work in
Can't make it? : We'll be recording the session and posting to our youtube channel (http://www.youtube.com/channel/UC7KVhK-ugN1iYrTT96-yGxQ).


Machine Learning From Disaster