Machine Learning: Difference between revisions
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We have weekly | Meetings are at at 2169 Mission St. We're currently voting on when to have the next weekly meeting: | ||
http://doodle.com/9w2x7vf3xvsz4k5h | |||
=== Topics to Learn and Teach === | |||
*Linear Regression | |||
*Linear Discriminants | |||
*Decision Theory (Bayesian) | |||
*Maximum Likelihood | |||
*Neural Nets/Radial Basis Functions | |||
*Bias/Variance Tradeoff, VC Dimension | |||
*Clustering/PCA | |||
*No-Free Lunch Theorem [http://www.cedar.buffalo.edu/~srihari/CSE555/Chap9.Part1.pdf] | |||
*Graphical Modeling | |||
*Support Vector Machines | |||
*k-Means Clustering | |||
*Reinforcement Learning | |||
*Bagging, Bootstrap, Jacknife [http://www.cedar.buffalo.edu/~srihari/CSE555/Chap9.Part3.pdf] | |||
*Generative Models: gaussian distribution, multinomial distributions, HMMs, Naive Bayes | |||
*Metric spaces and what they mean | |||
*Fundamentals of probabilities | |||
*Information Theory: Entroy, Mutual Information, Gaussian Channels | |||
*A basic decision tree builder, recursive and using entropy metrics | |||
*Estimation of Misclassification [http://www.cedar.buffalo.edu/~srihari/CSE555/Chap9.Part5.pdf] | |||
*Classifier Combination [http://www.cedar.buffalo.edu/~srihari/CSE555/Chap9.Part6.pdf] | |||
=== Notes from Meetings === | === Notes from Meetings === | ||
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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. | 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. | ||
Revision as of 09:52, 15 April 2010
Meetings are at at 2169 Mission St. We're currently voting on when to have the next weekly meeting:
http://doodle.com/9w2x7vf3xvsz4k5h
Topics to Learn and Teach
- Linear Regression
- Linear Discriminants
- Decision Theory (Bayesian)
- Maximum Likelihood
- Neural Nets/Radial Basis Functions
- Bias/Variance Tradeoff, VC Dimension
- Clustering/PCA
- No-Free Lunch Theorem [1]
- Graphical Modeling
- Support Vector Machines
- k-Means Clustering
- Reinforcement Learning
- Bagging, Bootstrap, Jacknife [2]
- Generative Models: gaussian distribution, multinomial distributions, HMMs, Naive Bayes
- Metric spaces and what they mean
- Fundamentals of probabilities
- Information Theory: Entroy, Mutual Information, Gaussian Channels
- A basic decision tree builder, recursive and using entropy metrics
- Estimation of Misclassification [3]
- Classifier Combination [4]
Notes from Meetings
Machine Learning Meetup Notes: 2010-04-14 -- (re)Starting new Machine Learning Meetup!
(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.