====== Machine Learning ======
**with Ed Borasky (@znmeb on twitter)**


==== What are the benefits of machine learning? ====
We can let the machine do its own work.
It's a hands off approach to managing.

==== Our interests in machine learning: ====
Natural Language Processing
Data Hacks
Genetic Algorithms
K Nearest neighbor algorithms
Clustering
Support Vector Machines
Scalability (a huge problem since some algorithms are in O(n^3) or worse time)

==== Projects we are working on or interested in: ====
Categorizing articles in RSS feeds to make a daily paper from the blogs you read.
Finding new, eye opening, news sources and having them brought to us.
Sentiment analysis-  Commercial applications of determining if someone has a positive or negative opinion of product that they are talking about.  This is a difficult problem, complicated by sarcasm and other language use factors.
How can machine bridge the correlation to causality gap?

==== Applications to Twitter: ====
Latent Semantic Analysis to reduce the last 200 tweets to simple commonalities using singular value decomposition, shared subjects.  We treat each persons tweets as a single document and make a matrix of the terms they used.  This can be very slow in R.
Bayesian classifiers to filter out annoying tweets.  Every tweet is run through a constant time calculation to determine is class.  RSS vs. Twitter as a data source.  Blogs are more focused on specific topics.

==== What were the ideas behind the Netflix contest? ====
 
The benefit to Netflix is in the hundreds of millions of dollars.

==== Next Steps: ====


Come see the Write Your Own Bayesian Classifier talk by John Meleski at Open Source Bridge.

Possibility of an R language school that would meet twice.  First day how to install and set up R.  Second day, doing some modeling and data analysis.


==== Further Reading: ====

Toby Segaran’s book, "Programming Collective Intelligence" (O'Reilly, 2007).
?ADD that blog here?

==== Tools: ====


R has a comprehensive NLP library that allows clustering and other techniques.
Python Helpers Libraries for Faster Numeric Computing:
scipy
numpy