Machine Learning: Difference between revisions
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=== Topics to Learn and Teach === | === Topics to Learn and Teach === | ||
*Linear Regression (Mike S volunteered to teach) | *Supervised Learning | ||
*Linear Discriminants | **Linear Regression (Mike S volunteered to teach) | ||
* | **Linear Discriminants | ||
**Neural Nets/Radial Basis Functions | |||
*Neural Nets/Radial Basis Functions | **Support Vector Machines | ||
* | **Classifier Combination [http://www.cedar.buffalo.edu/~srihari/CSE555/Chap9.Part6.pdf] | ||
* | |||
* | *Unsupervised Learning | ||
* | **Clustering/PCA | ||
* | **k-Means Clustering | ||
*k-Means Clustering | **Graphical Modeling | ||
*Reinforcement Learning | *Reinforcement Learning | ||
*Bagging, Bootstrap, Jacknife [http://www.cedar.buffalo.edu/~srihari/CSE555/Chap9.Part3.pdf] | **Temporal Difference Learning | ||
* | |||
*Math, Probability & Statistics | |||
**Metric spaces and what they mean | |||
*Information Theory: | **Fundamentals of probabilities | ||
* | **Decision Theory (Bayesian) | ||
*Estimation of Misclassification [http://www.cedar.buffalo.edu/~srihari/CSE555/Chap9.Part5.pdf] | **Maximum Likelihood | ||
* | **Bias/Variance Tradeoff, VC Dimension | ||
**Bagging, Bootstrap, Jacknife [http://www.cedar.buffalo.edu/~srihari/CSE555/Chap9.Part3.pdf] | |||
**Information Theory: Entropy, Mutual Information, Gaussian Channels | |||
**Estimation of Misclassification [http://www.cedar.buffalo.edu/~srihari/CSE555/Chap9.Part5.pdf] | |||
*Miscellaneous | |||
**No-Free Lunch Theorem [http://www.cedar.buffalo.edu/~srihari/CSE555/Chap9.Part1.pdf] | |||
**Generative Models: gaussian distribution, multinomial distributions, HMMs, Naive Bayes | |||
**A basic decision tree builder, recursive and using entropy metrics | |||
Revision as of 10:07, 15 April 2010
Come to the ML-Meetup @ Noisebridge
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
- Supervised Learning
- Linear Regression (Mike S volunteered to teach)
- Linear Discriminants
- Neural Nets/Radial Basis Functions
- Support Vector Machines
- Classifier Combination [1]
- Unsupervised Learning
- Clustering/PCA
- k-Means Clustering
- Graphical Modeling
- Reinforcement Learning
- Temporal Difference Learning
- Math, Probability & Statistics
- Miscellaneous
- No-Free Lunch Theorem [4]
- Generative Models: gaussian distribution, multinomial distributions, HMMs, Naive Bayes
- A basic decision tree builder, recursive and using entropy metrics
Possible Projects
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.
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