Yes, I took that class and really enjoyed it. I'll be basing much of
On Mon, Jun 18, 2012 at 11:40 PM, Bryan Wolfford <br...
> Did you do the ML-Class from Standford under Dr. Ng as well?
> From: firstname.lastname@example.org
> [mailto:email@example.com] On Behalf Of Kyle Oba
> Sent: Monday, June 18, 2012 6:43 PM
> To: firstname.lastname@example.org
> Subject: [HI Cap] OUDL: Introduction to Machine Learning
> Kevin McCarthy will present a gentle introduction to 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
> 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