Introduction to Machine Learning
- June 26, 2012 · 7:00 PM
Agenda:
1) Intro + refreshments
2) Before the Machine Learning talk, William Ting will present a lightning talk on Autojump.
3) Machine Learning
Kevin McCarthy will present a gentle introduction to Machine Learning*.
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!
This group is participatory and run from the ground up. We *require* your suggestions and votes. Please take a moment to vote now:
https://dynamic.uservoice.com/
"The foolish man seeks happiness in the distance, the wise grows it under his feet."
- James Oppenheim
*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.
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Shu Zong Chen
Some other resources for machine learning:
Here's a cool 20min talk by a bitly scientist: http://vimeo.com/43547079 (it's a little more general overview then the one Kevin gave)
but what's cool is she gave a link to a bunch of cool datasets you can download and use when you're playing with machine learning things:
https://bitly.com/bundles/hmason...July 4, 2012
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Kevin McCarthy
OK github doesn't allow direct downloads of the slide PDF for some reason so I dropboxed them: https://dl.dropbox.com/u/379898/...
June 27, 2012
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Kevin McCarthy
Here's more on the DBSCAN clustering algorithm Ethan was asking about: http://en.wikipedia.org/wiki/DB...
1 · June 27, 2012
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Kevin McCarthy
Slides are here: https://github.com/kevin1024/ml_...
Someone asked for a link to the tools I was using in the demo. It was mostly numpy, scipy, and matplotlib which were displayed using an ipython notebook. Here's how to get that set up:
http://ipython.org/ipython-doc/...
A really cool python machine learning library is scikit-learn: http://scikit-learn.org/stable/
1 · June 27, 2012
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Bobby +1
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A former member
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