Machine Learning for everyone

Well maybe mostly everyone. Have  you been interested in gaining knowledge in the latest craze of artificial intelligence and computing?  Then go no further than Stanford's Machine Learning course which is now open enrollment to everyone!  Andrew Ng is back to provide the world with knowledge about Machine Learning for the entire masses. 

Per Stanford's website, Machine Learning is data mining and statistical pattern recognition.  Mostly it is applying mathematical and statistical methods to draw out information behaviors from data sources.  So do you want to invent the next Netflix, Amazon or Google?  This is the course for you.

If you do not want to enroll in the Machine Learning class you could always watch some of the older lectures online.  Andrew Ng provides plenty of information from past lectures with student contributed projects.  The CS 229 website is worth a look for a punch of Machine Learning related resources.

Data Driven Success in Professional Baseball

An interesting article from Data Center Knowledge about the presentation Paul DePodesta gave at the Strata Summit.  Paul DePodesta is known for bringing mathematic and analytical know-how to Billy Beane and the major league professional baseball team Oakland Athletics.  His story was accounted by Michael Lewis in the book "Moneyball" and is being portrayed with the same name on the big screen opening this weekend.

I really liked this quote from Paul in the article.

We didn’t solve baseball. But we reduced the inefficiency of our decision making.

Is that not the sort of things that an analytical professional or an Operations Researcher ultimately tries to do?   Operations Research is not the art of creating anything new.  It is the art of creating existing things better.  All decision making is inefficient to some point.  Even the right decision can be inefficient on some level.  Decisions are full of balancing acts of constraints and feasibility.

Also this proves that no industry or organization is absent of a need for efficient decision making.  Even baseball can us a dose of improved decision analysis.  Whether is scheduling the league or determining the best pitcher for their value.  Sports has definitely come into their own with decision analytics.  I'm eager to watch Paul's career and wonder if analytics is taking it to the next level.

OpenOpt Suite 0.36

New release of the free BSD-licensed software OpenOpt Suite is out:

OpenOpt:

* Now solver interalg can handle all types of constraints and integration problems

* Some minor improvements and code cleanup

FuncDesigner:

* Interval analysis now can involve min, max and 1-d monotone splines R -> R of 1st and 3rd order

* Some bugfixes and improvements

SpaceFuncs:

* Some minor changes

DerApproximator:

* Some improvements for obtaining derivatives in points from R^n where left or right derivative for a variable is absent, especially for stencil > 1

See http://openopt.org for more details.