Machine Learning
This is a placeholder landing page for Machine Learning related projects and topics at noisebridge.
We have weekly meetings, every Sunday at 5PM, at 2169 Mission St.
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