# Machine Learning

This is a placeholder landing page for Machine Learning related projects and topics at noisebridge.

We have weekly meetings, every Wednesday at 8PM, at 83c.

## Contents |

### Notes from Meetings

(We've fallen off the notes bandwagon, sorry.)

Machine Learning Meetup Notes: 2009-04-01 -- Finally moving on up: fully-connected backpropagation networks.

Machine Learning Meetup Notes: 2009-03-25 -- We made perceptrons - added sigmoid, etc.

Machine Learning Meetup Notes: 2009-03-18 -- We made perceptrons - linear function support!

Machine Learning Meetup Notes: 2009-03-11 -- We made perceptrons!

Machine Learning Meetup Notes: 2009-03-04 -- Josh gave a presentation on SVMs

(time is missing!)

Machine Learning Meetup Notes: 2009-02-11 -- Josh gave a presentation on clustering, donuts!

Machine Learning Meetup Notes: 2009-02-04 -- Free-form hang out night, punch and pie

Machine Learning Meetup Notes: 2009-01-28 -- Praveen talked about Neural networks

Machine Learning Meetup Notes: 2008-01-21 -- Jean gave a quick overview of machine learning stuff

Machine Learning Meetup Notes: 2009-01-14 -- Ian gave a talk on k-Nearest Neighbor

Machine Learning Meetup Notes: 2009-01-07 -- Josh did a quick intro to ML presentation

Machine Learning Meetup Notes: 2008-12-17

### Presentations and other Materials

- Awesome Machine Learning Applications -- A list of cool applications of ML
- Hands-on Machine Learning, a presentation jbm gave on 2009-01-07.

### Possible topics

It would be nice to have a cache of topics we might want to discuss at future meetings; this is a placeholder to keep track of them. If you'd like to present on a Wednesday, but aren't sure what to do it on, consider researching one of these topics and presenting that.

- No-Free Lunch Theorem [1]
- Bias and Variance [2]
- Resampling for Estimation [3]
- Bagging and Boosting [4]
- Estimation of Misclassification [5]
- Classifier Combination [6]

- Entropy in the information-theoretic sense
- A basic decision tree builder, recursive and using entropy metrics

- Metric spaces and what they mean

- Fundamentals of probabilities
- Naive Bayes classification

Please add things here as you think of them, with or without supporting documentation.

### Possible Projects

- Gesture recognition using a Wiimote