Kevin McCarthy will present a gentle introduction to Machine Learning<http://en.wikipedia.org/wiki/Machine_learning>
.

Note day and location change, just for this meeting.

Tuesday, 6/26, 7pm - Interisland Terminal R/D

691 Auahi St., Honolulu, HI 96813 - http://goo.gl/maps/aTvk

RSVP Here: http://www.meetup.com/dynamic/events/69285152/

Have you ever wished your computer could do more than what you tell it
to do explicitly?  Maybe you want to write a recommendation engine
like the one Amazon and Netflix use to recommend similar products, or
maybe you just want to build Skynet. The goal of this talk is to
give a broad but shallow overview of machine learning techniques and
applications.  Topics covered will (probably) include:

- What is machine learning?
- Supervised vs unsupervised machine learning
- Linear Regression
- Partitioning your data into training, test, and cross-validation sets
- Bias/variance tradeoff
- Regularization
- Logistic Regression
- Clustering
- Brief overview of more advanced algorithms such as neural networks
and support vector machines
- Advanced applications such as digit recognition and collaborative
filtering

Should be fun!

More on Machine Learning:

Machine learning, a branch of artificial intelligence, is a scientific
discipline concerned with the design and development of algorithms that
allow computers to evolve behaviors based on empirical data, such as from
sensor data or databases. A learner can take advantage of examples (data)
to capture characteristics of interest of their unknown underlying
probability distribution. Data can be seen as examples that illustrate
relations between observed variables. A major focus of machine learning
research is to automatically learn to recognize complex patterns and make
intelligent decisions based on data; the difficulty lies in the fact that
the set of all possible behaviors given all possible inputs is too large to
be covered by the set of observed examples (training data). Hence the
learner must generalize from the given examples, so as to be able to
produce a useful output in new cases.

From Wikipedia <http://en.wikipedia.org/wiki/Machine_learning>