Noisebridge - User contributions [en]

Machine Learning

2011-10-17T01:07:43Z

76.126.173.82: /* Next Meeting */

=== Next Meeting===

*When: Monday, 10/17/2011 @ 7:30-9:00pm
*Where: 2169 Mission St. (back corner, Church classroom)
*Topic: Working through [http://www.ml-class.org/course/quiz/list?type=quiz review] for [http://www.ml-class.org Stanford's ML Class]
*Details:

=== Take the Noisebridge ML Survey ===
[http://www.surveymonkey.com/s/W2T9ZB6 Take a survey] and vote for what you want to learn!

=== Crowdsourced Q&A ===
Are you working on a data mining, machine learning, or statistics problem? Do you want some help? Consider sending an email to the [https://www.noisebridge.net/mailman/listinfo/ml mailing list] about it! Also consider setting up a day to come in and talk about the project you're working on and get input from other ML people.

=== About Us ===
We're a loosely-knit stochastic federation of people who like Noisebridge and like machine learning. What is machine learning? It's broad field that typically involves training computer models to solve problems. How can you participate? Join the [https://www.noisebridge.net/mailman/listinfo/ml mailing list], send an email and introduce yourself. Show up to the next meeting, share your thoughts. Participate in projects or start your own. Go to workshops, write code at workshops, learn stuff, give workshops of your own! All are welcome.

=== Talks and Workshops ===
We've given lots of workshops and talks over the past year or so, here's a few. Many of the workshops we've given previously are recurring and will be given again, especially upon request!
*[[NBML/Workshops/Intro to Machine Learning|Intro to Machine Learning]]
*[[NBML/Workshops/Brief Tour of Statistics|A Brief Tour of Statistics]]
*[[NBML/Workshops/Generalized Linear Models|Generalized Linear Models]]
*[[NBML/Workshops/Neural Nets|Neural Nets Workshop]]
*[[NBML/Workshops/Support Vector Machines|Support Vector Machines]]
*[[NBML/Workshops/Random Forests|Random Forests]]
*[[NBML/Workshops/Independent Components Analysis|Independent Components Analysis]]
*[[NBML/Workshops/Deep Nets|Deep Nets]]

=== Code and SourceForge Site ===
*We have a [http://sourceforge.net/projects/ml-noisebridge Sourceforge Project]
*We have a git repository on the project page, accessible as:
git clone git://ml-noisebridge.git.sourceforge.net/gitroot/ml-noisebridge/ml-noisebridge
*Send an email to the list if you want to become an administrator on the site to get write access to the git repo!

=== Future Talks and Topics, Ideas ===
*Restricted Boltzmann Machines (Mike S, late August)
*Deep Nets w/ Stacked Autoencoders (Mike S, September)
*Generalized Linear Models (Mike S, September/October)
*Graphical Models (Tony)
*Working with the Kinect
*Computer Vision with OpenCV

=== Mailing List ===

https://www.noisebridge.net/mailman/listinfo/ml

=== Projects ===
*[[Small Group Subproblems]]
*[[Machine Learning/Fundraising | Fundraising]]
*[[NBML_Course|Noisebridge Machine Learning Course]]
*[[Machine Learning/Kaggle Social Network Contest | Kaggle Social Network Contest]]
*[[KDD Competition 2010]]
*[[Machine Learning/Kaggle HIV | HIV]]

=== [[Machine_Learning/Datasets|Datasets and Websites]] ===
*[http://archive.ics.uci.edu/ml/ UCI Machine Learning Repository]
*[[DataSF.org]]
*[http://infochimps.com/ Infochimps]
*[http://www.face-rec.org/databases/ Face Recognition Databases]
*[http://robjhyndman.com/TSDL/ Time Series Data Library]
*[http://getthedata.org/ Data Q&A Forum]
*[http://metaoptimize.com/qa/ Metaoptimize]
*[http://www.quora.com/Machine-Learning Quora ML Page]
*[http://www.metoffice.gov.uk/research/climate/climate-monitoring/land-and-atmosphere/surface-station-records A ton of Weather Data]
*[http://mlcomp.org/ MLcomp]
**Upload your algorithm and objectively compare it's performance to other algorithms

=== [[Machine Learning/Tools | Software Tools]] ===
*[http://opencv.willowgarage.com/documentation/index.html OpenCV]
**Computer Vision Library
**Has ML component (SVM, trees, etc)
**Online tutorials [http://www.pages.drexel.edu/~nk752/tutorials.html here]
*[http://lucene.apache.org/mahout/ Mahout]
**Hadoop cluster based ML package.
*[http://www.cs.waikato.ac.nz/ml/weka/ Weka]
**a collection of data mining tools and machine learning algorithms.
*[http://moa.cs.waikato.ac.nz/ MOA (Massive Online Analysis)]
**Offshoot of weka, has all online-algorithms
*[http://scikit-learn.sourceforge.net/ scikits.learn]
**Machine learning Python package
*[http://pybrain.org PyBrain]
**Does feedforward, recurrent, SOM, deep belief nets.
*[http://www.csie.ntu.edu.tw/~cjlin/libsvm/ LIBSVM]
**c-based SVM package
*[http://pyml.sourceforge.net PyML]
*[http://mdp-toolkit.sourceforge.net/ MDP]
**Does not stand for Markov Decision Process :(
*[http://www.ailab.si/orange/ Orange]
**Strong data visualization component
*[http://jmlr.csail.mit.edu/mloss/ Journal of Machine Learning Software List]
*[[Machine Learning/VirtualBox|VirtualBox]] Virtual Box Image with Pre-installed Libraries listed here
*[http://deeplearning.net/software/theano/ Theano: Symbolic Expressions and Transparent GPU Integration]
*[http://waffles.sourceforge.net/ Waffles]
**Open source C++ set of machine learning command line tools.
*[http://rapid-i.com/content/view/181/196/ RapidMiner]
*[http://www.mrpt.org/ Mobile Robotic Programming Toolkit]
*[http://gephi.org/ Gephi]
**Graph Visualization
*[http://had.co.nz/ggplot2/ ggplot]
**Nice plotting package for R
*[http://nipy.sourceforge.net/nitime/ nitime]
**NeuroImaging in Python, has some good time series analysis stuff and multi-variate response fitting.

=== Presentations and other Materials ===
* [[Awesome Machine Learning Applications]] -- A list of cool applications of ML
* [[Hands-on Machine Learning]], a presentation [[User:jbm|jbm]] gave on 2009-01-07.
* http://www.youtube.com/user/StanfordUniversity#g/c/A89DCFA6ADACE599 Stanford Machine Learning online course videos]
* [[Media:Brief_statistics_slides.pdf]], a presentation given on statistics for the machine learning group
* [http://www.linkedin.com/groupAnswers?viewQuestionAndAnswers=&discussionID=20096092&gid=77616&trk=EML_anet_qa_ttle-0Nt79xs2RVr6JBpnsJt7dBpSBA LinkedIn] discussion on good resources for data mining and predictive analytics
* [http://www.face-rec.org/algorithms/ Face Recognition Algorithms]
* [http://www.ics.uci.edu/~welling/classnotes/classnotes.html Max Welling's ML classnotes]

=== Topics to Learn and Teach ===
[[NBML Course]] - Noisebridge Machine Learning Curriculum (work-in-progress)

[[CS229]] - The Stanford Machine learning Course @ noisebridge

*Supervised Learning
**Linear Regression
**Linear Discriminants
**Neural Nets/Radial Basis Functions
**Support Vector Machines
**Classifier Combination [http://www.cedar.buffalo.edu/~srihari/CSE555/Chap9.Part6.pdf]
**A basic decision tree builder, recursive and using entropy metrics

*Unsupervised Learning
**[[Machine_Learning/HMM|Hidden Markov Models]]
**Clustering: PCA, k-Means, Expectation-Maximization
**Graphical Modeling
**Generative Models: gaussian distribution, multinomial distributions, HMMs, Naive Bayes
**[[Machine_Learning/Deep_Belief_Networks|Deep Belief Networks & Restricted Boltzmann Machines]]

*Reinforcement Learning
**Temporal Difference Learning

*Math, Probability & Statistics
**Metric spaces and what they mean
**Fundamentals of probabilities
**Decision Theory (Bayesian)
**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]
**No-Free Lunch Theorem [http://www.cedar.buffalo.edu/~srihari/CSE555/Chap9.Part1.pdf]

*Machine Learning SDK's
** [http://opencv.willowgarage.com/documentation/index.html OpenCV] ML component (SVM, trees, etc)
**[http://lucene.apache.org/mahout/ Mahout] a Hadoop cluster based ML package.
**[http://www.cs.waikato.ac.nz/ml/weka/ Weka] a collection of data mining tools and machine learning algorithms.

*Applications
** Collective Intelligence & Recommendation Engines

=== [[Machine Learning/Meeting Notes|Meeting Notes]]===

Machine Learning

2011-10-17T01:07:30Z

76.126.173.82:

=== Next Meeting===

*When: Monday, 10/17/2011 @ 7:00-8:30pm
*Where: 2169 Mission St. (back corner, Church classroom)
*Topic: Working through [http://www.ml-class.org/course/quiz/list?type=quiz review] for [http://www.ml-class.org Stanford's ML Class]
*Details:

=== Take the Noisebridge ML Survey ===
[http://www.surveymonkey.com/s/W2T9ZB6 Take a survey] and vote for what you want to learn!

=== Crowdsourced Q&A ===
Are you working on a data mining, machine learning, or statistics problem? Do you want some help? Consider sending an email to the [https://www.noisebridge.net/mailman/listinfo/ml mailing list] about it! Also consider setting up a day to come in and talk about the project you're working on and get input from other ML people.

=== About Us ===
We're a loosely-knit stochastic federation of people who like Noisebridge and like machine learning. What is machine learning? It's broad field that typically involves training computer models to solve problems. How can you participate? Join the [https://www.noisebridge.net/mailman/listinfo/ml mailing list], send an email and introduce yourself. Show up to the next meeting, share your thoughts. Participate in projects or start your own. Go to workshops, write code at workshops, learn stuff, give workshops of your own! All are welcome.

=== Talks and Workshops ===
We've given lots of workshops and talks over the past year or so, here's a few. Many of the workshops we've given previously are recurring and will be given again, especially upon request!
*[[NBML/Workshops/Intro to Machine Learning|Intro to Machine Learning]]
*[[NBML/Workshops/Brief Tour of Statistics|A Brief Tour of Statistics]]
*[[NBML/Workshops/Generalized Linear Models|Generalized Linear Models]]
*[[NBML/Workshops/Neural Nets|Neural Nets Workshop]]
*[[NBML/Workshops/Support Vector Machines|Support Vector Machines]]
*[[NBML/Workshops/Random Forests|Random Forests]]
*[[NBML/Workshops/Independent Components Analysis|Independent Components Analysis]]
*[[NBML/Workshops/Deep Nets|Deep Nets]]

=== Code and SourceForge Site ===
*We have a [http://sourceforge.net/projects/ml-noisebridge Sourceforge Project]
*We have a git repository on the project page, accessible as:
git clone git://ml-noisebridge.git.sourceforge.net/gitroot/ml-noisebridge/ml-noisebridge
*Send an email to the list if you want to become an administrator on the site to get write access to the git repo!

=== Future Talks and Topics, Ideas ===
*Restricted Boltzmann Machines (Mike S, late August)
*Deep Nets w/ Stacked Autoencoders (Mike S, September)
*Generalized Linear Models (Mike S, September/October)
*Graphical Models (Tony)
*Working with the Kinect
*Computer Vision with OpenCV

=== Mailing List ===

https://www.noisebridge.net/mailman/listinfo/ml

=== Projects ===
*[[Small Group Subproblems]]
*[[Machine Learning/Fundraising | Fundraising]]
*[[NBML_Course|Noisebridge Machine Learning Course]]
*[[Machine Learning/Kaggle Social Network Contest | Kaggle Social Network Contest]]
*[[KDD Competition 2010]]
*[[Machine Learning/Kaggle HIV | HIV]]

=== [[Machine_Learning/Datasets|Datasets and Websites]] ===
*[http://archive.ics.uci.edu/ml/ UCI Machine Learning Repository]
*[[DataSF.org]]
*[http://infochimps.com/ Infochimps]
*[http://www.face-rec.org/databases/ Face Recognition Databases]
*[http://robjhyndman.com/TSDL/ Time Series Data Library]
*[http://getthedata.org/ Data Q&A Forum]
*[http://metaoptimize.com/qa/ Metaoptimize]
*[http://www.quora.com/Machine-Learning Quora ML Page]
*[http://www.metoffice.gov.uk/research/climate/climate-monitoring/land-and-atmosphere/surface-station-records A ton of Weather Data]
*[http://mlcomp.org/ MLcomp]
**Upload your algorithm and objectively compare it's performance to other algorithms

=== [[Machine Learning/Tools | Software Tools]] ===
*[http://opencv.willowgarage.com/documentation/index.html OpenCV]
**Computer Vision Library
**Has ML component (SVM, trees, etc)
**Online tutorials [http://www.pages.drexel.edu/~nk752/tutorials.html here]
*[http://lucene.apache.org/mahout/ Mahout]
**Hadoop cluster based ML package.
*[http://www.cs.waikato.ac.nz/ml/weka/ Weka]
**a collection of data mining tools and machine learning algorithms.
*[http://moa.cs.waikato.ac.nz/ MOA (Massive Online Analysis)]
**Offshoot of weka, has all online-algorithms
*[http://scikit-learn.sourceforge.net/ scikits.learn]
**Machine learning Python package
*[http://pybrain.org PyBrain]
**Does feedforward, recurrent, SOM, deep belief nets.
*[http://www.csie.ntu.edu.tw/~cjlin/libsvm/ LIBSVM]
**c-based SVM package
*[http://pyml.sourceforge.net PyML]
*[http://mdp-toolkit.sourceforge.net/ MDP]
**Does not stand for Markov Decision Process :(
*[http://www.ailab.si/orange/ Orange]
**Strong data visualization component
*[http://jmlr.csail.mit.edu/mloss/ Journal of Machine Learning Software List]
*[[Machine Learning/VirtualBox|VirtualBox]] Virtual Box Image with Pre-installed Libraries listed here
*[http://deeplearning.net/software/theano/ Theano: Symbolic Expressions and Transparent GPU Integration]
*[http://waffles.sourceforge.net/ Waffles]
**Open source C++ set of machine learning command line tools.
*[http://rapid-i.com/content/view/181/196/ RapidMiner]
*[http://www.mrpt.org/ Mobile Robotic Programming Toolkit]
*[http://gephi.org/ Gephi]
**Graph Visualization
*[http://had.co.nz/ggplot2/ ggplot]
**Nice plotting package for R
*[http://nipy.sourceforge.net/nitime/ nitime]
**NeuroImaging in Python, has some good time series analysis stuff and multi-variate response fitting.

=== Presentations and other Materials ===
* [[Awesome Machine Learning Applications]] -- A list of cool applications of ML
* [[Hands-on Machine Learning]], a presentation [[User:jbm|jbm]] gave on 2009-01-07.
* http://www.youtube.com/user/StanfordUniversity#g/c/A89DCFA6ADACE599 Stanford Machine Learning online course videos]
* [[Media:Brief_statistics_slides.pdf]], a presentation given on statistics for the machine learning group
* [http://www.linkedin.com/groupAnswers?viewQuestionAndAnswers=&discussionID=20096092&gid=77616&trk=EML_anet_qa_ttle-0Nt79xs2RVr6JBpnsJt7dBpSBA LinkedIn] discussion on good resources for data mining and predictive analytics
* [http://www.face-rec.org/algorithms/ Face Recognition Algorithms]
* [http://www.ics.uci.edu/~welling/classnotes/classnotes.html Max Welling's ML classnotes]

=== Topics to Learn and Teach ===
[[NBML Course]] - Noisebridge Machine Learning Curriculum (work-in-progress)

[[CS229]] - The Stanford Machine learning Course @ noisebridge

*Supervised Learning
**Linear Regression
**Linear Discriminants
**Neural Nets/Radial Basis Functions
**Support Vector Machines
**Classifier Combination [http://www.cedar.buffalo.edu/~srihari/CSE555/Chap9.Part6.pdf]
**A basic decision tree builder, recursive and using entropy metrics

*Unsupervised Learning
**[[Machine_Learning/HMM|Hidden Markov Models]]
**Clustering: PCA, k-Means, Expectation-Maximization
**Graphical Modeling
**Generative Models: gaussian distribution, multinomial distributions, HMMs, Naive Bayes
**[[Machine_Learning/Deep_Belief_Networks|Deep Belief Networks & Restricted Boltzmann Machines]]

*Reinforcement Learning
**Temporal Difference Learning

*Math, Probability & Statistics
**Metric spaces and what they mean
**Fundamentals of probabilities
**Decision Theory (Bayesian)
**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]
**No-Free Lunch Theorem [http://www.cedar.buffalo.edu/~srihari/CSE555/Chap9.Part1.pdf]

*Machine Learning SDK's
** [http://opencv.willowgarage.com/documentation/index.html OpenCV] ML component (SVM, trees, etc)
**[http://lucene.apache.org/mahout/ Mahout] a Hadoop cluster based ML package.
**[http://www.cs.waikato.ac.nz/ml/weka/ Weka] a collection of data mining tools and machine learning algorithms.

*Applications
** Collective Intelligence & Recommendation Engines

=== [[Machine Learning/Meeting Notes|Meeting Notes]]===

Machine Learning

2011-10-06T20:24:00Z

76.126.173.82:

=== Next Meeting===

*When: Monday, 10/10/2011 @ 7:00-8:30pm
*Where: 2169 Mission St. (back corner, Church classroom)
*Topic: Working through [http://www.ml-class.org/course/quiz/list?type=quiz review] for [http://www.ml-class.org Stanford's ML Class]
*Details:

=== Take the Noisebridge ML Survey ===
[http://www.surveymonkey.com/s/W2T9ZB6 Take a survey] and vote for what you want to learn!

=== Crowdsourced Q&A ===
Are you working on a data mining, machine learning, or statistics problem? Do you want some help? Consider sending an email to the [https://www.noisebridge.net/mailman/listinfo/ml mailing list] about it! Also consider setting up a day to come in and talk about the project you're working on and get input from other ML people.

=== About Us ===
We're a loosely-knit stochastic federation of people who like Noisebridge and like machine learning. What is machine learning? It's broad field that typically involves training computer models to solve problems. How can you participate? Join the [https://www.noisebridge.net/mailman/listinfo/ml mailing list], send an email and introduce yourself. Show up to the next meeting, share your thoughts. Participate in projects or start your own. Go to workshops, write code at workshops, learn stuff, give workshops of your own! All are welcome.

=== Talks and Workshops ===
We've given lots of workshops and talks over the past year or so, here's a few. Many of the workshops we've given previously are recurring and will be given again, especially upon request!
*[[NBML/Workshops/Intro to Machine Learning|Intro to Machine Learning]]
*[[NBML/Workshops/Brief Tour of Statistics|A Brief Tour of Statistics]]
*[[NBML/Workshops/Generalized Linear Models|Generalized Linear Models]]
*[[NBML/Workshops/Neural Nets|Neural Nets Workshop]]
*[[NBML/Workshops/Support Vector Machines|Support Vector Machines]]
*[[NBML/Workshops/Random Forests|Random Forests]]
*[[NBML/Workshops/Independent Components Analysis|Independent Components Analysis]]
*[[NBML/Workshops/Deep Nets|Deep Nets]]

=== Code and SourceForge Site ===
*We have a [http://sourceforge.net/projects/ml-noisebridge Sourceforge Project]
*We have a git repository on the project page, accessible as:
git clone git://ml-noisebridge.git.sourceforge.net/gitroot/ml-noisebridge/ml-noisebridge
*Send an email to the list if you want to become an administrator on the site to get write access to the git repo!

=== Future Talks and Topics, Ideas ===
*Restricted Boltzmann Machines (Mike S, late August)
*Deep Nets w/ Stacked Autoencoders (Mike S, September)
*Generalized Linear Models (Mike S, September/October)
*Graphical Models (Tony)
*Working with the Kinect
*Computer Vision with OpenCV

=== Mailing List ===

https://www.noisebridge.net/mailman/listinfo/ml

=== Projects ===
*[[Small Group Subproblems]]
*[[Machine Learning/Fundraising | Fundraising]]
*[[NBML_Course|Noisebridge Machine Learning Course]]
*[[Machine Learning/Kaggle Social Network Contest | Kaggle Social Network Contest]]
*[[KDD Competition 2010]]
*[[Machine Learning/Kaggle HIV | HIV]]

=== [[Machine_Learning/Datasets|Datasets and Websites]] ===
*[http://archive.ics.uci.edu/ml/ UCI Machine Learning Repository]
*[[DataSF.org]]
*[http://infochimps.com/ Infochimps]
*[http://www.face-rec.org/databases/ Face Recognition Databases]
*[http://robjhyndman.com/TSDL/ Time Series Data Library]
*[http://getthedata.org/ Data Q&A Forum]
*[http://metaoptimize.com/qa/ Metaoptimize]
*[http://www.quora.com/Machine-Learning Quora ML Page]
*[http://www.metoffice.gov.uk/research/climate/climate-monitoring/land-and-atmosphere/surface-station-records A ton of Weather Data]
*[http://mlcomp.org/ MLcomp]
**Upload your algorithm and objectively compare it's performance to other algorithms

=== [[Machine Learning/Tools | Software Tools]] ===
*[http://opencv.willowgarage.com/documentation/index.html OpenCV]
**Computer Vision Library
**Has ML component (SVM, trees, etc)
**Online tutorials [http://www.pages.drexel.edu/~nk752/tutorials.html here]
*[http://lucene.apache.org/mahout/ Mahout]
**Hadoop cluster based ML package.
*[http://www.cs.waikato.ac.nz/ml/weka/ Weka]
**a collection of data mining tools and machine learning algorithms.
*[http://moa.cs.waikato.ac.nz/ MOA (Massive Online Analysis)]
**Offshoot of weka, has all online-algorithms
*[http://scikit-learn.sourceforge.net/ scikits.learn]
**Machine learning Python package
*[http://pybrain.org PyBrain]
**Does feedforward, recurrent, SOM, deep belief nets.
*[http://www.csie.ntu.edu.tw/~cjlin/libsvm/ LIBSVM]
**c-based SVM package
*[http://pyml.sourceforge.net PyML]
*[http://mdp-toolkit.sourceforge.net/ MDP]
**Does not stand for Markov Decision Process :(
*[http://www.ailab.si/orange/ Orange]
**Strong data visualization component
*[http://jmlr.csail.mit.edu/mloss/ Journal of Machine Learning Software List]
*[[Machine Learning/VirtualBox|VirtualBox]] Virtual Box Image with Pre-installed Libraries listed here
*[http://deeplearning.net/software/theano/ Theano: Symbolic Expressions and Transparent GPU Integration]
*[http://waffles.sourceforge.net/ Waffles]
**Open source C++ set of machine learning command line tools.
*[http://rapid-i.com/content/view/181/196/ RapidMiner]
*[http://www.mrpt.org/ Mobile Robotic Programming Toolkit]
*[http://gephi.org/ Gephi]
**Graph Visualization
*[http://had.co.nz/ggplot2/ ggplot]
**Nice plotting package for R
*[http://nipy.sourceforge.net/nitime/ nitime]
**NeuroImaging in Python, has some good time series analysis stuff and multi-variate response fitting.

=== Presentations and other Materials ===
* [[Awesome Machine Learning Applications]] -- A list of cool applications of ML
* [[Hands-on Machine Learning]], a presentation [[User:jbm|jbm]] gave on 2009-01-07.
* http://www.youtube.com/user/StanfordUniversity#g/c/A89DCFA6ADACE599 Stanford Machine Learning online course videos]
* [[Media:Brief_statistics_slides.pdf]], a presentation given on statistics for the machine learning group
* [http://www.linkedin.com/groupAnswers?viewQuestionAndAnswers=&discussionID=20096092&gid=77616&trk=EML_anet_qa_ttle-0Nt79xs2RVr6JBpnsJt7dBpSBA LinkedIn] discussion on good resources for data mining and predictive analytics
* [http://www.face-rec.org/algorithms/ Face Recognition Algorithms]
* [http://www.ics.uci.edu/~welling/classnotes/classnotes.html Max Welling's ML classnotes]

=== Topics to Learn and Teach ===
[[NBML Course]] - Noisebridge Machine Learning Curriculum (work-in-progress)

[[CS229]] - The Stanford Machine learning Course @ noisebridge

*Supervised Learning
**Linear Regression
**Linear Discriminants
**Neural Nets/Radial Basis Functions
**Support Vector Machines
**Classifier Combination [http://www.cedar.buffalo.edu/~srihari/CSE555/Chap9.Part6.pdf]
**A basic decision tree builder, recursive and using entropy metrics

*Unsupervised Learning
**[[Machine_Learning/HMM|Hidden Markov Models]]
**Clustering: PCA, k-Means, Expectation-Maximization
**Graphical Modeling
**Generative Models: gaussian distribution, multinomial distributions, HMMs, Naive Bayes
**[[Machine_Learning/Deep_Belief_Networks|Deep Belief Networks & Restricted Boltzmann Machines]]

*Reinforcement Learning
**Temporal Difference Learning

*Math, Probability & Statistics
**Metric spaces and what they mean
**Fundamentals of probabilities
**Decision Theory (Bayesian)
**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]
**No-Free Lunch Theorem [http://www.cedar.buffalo.edu/~srihari/CSE555/Chap9.Part1.pdf]

*Machine Learning SDK's
** [http://opencv.willowgarage.com/documentation/index.html OpenCV] ML component (SVM, trees, etc)
**[http://lucene.apache.org/mahout/ Mahout] a Hadoop cluster based ML package.
**[http://www.cs.waikato.ac.nz/ml/weka/ Weka] a collection of data mining tools and machine learning algorithms.

*Applications
** Collective Intelligence & Recommendation Engines

=== [[Machine Learning/Meeting Notes|Meeting Notes]]===

Machine Learning

2011-10-06T20:23:48Z

76.126.173.82:

=== Next Meeting===

*When: Monday, 10/10/2011 @ 7:00-8:30pm
*Where: 2169 Mission St. (back corner, Church classroom)
*Topic: Working through [
http://www.ml-class.org/course/quiz/list?type=quiz review] for [http://www.ml-class.org Stanford's ML Class]
*Details:

=== Take the Noisebridge ML Survey ===
[http://www.surveymonkey.com/s/W2T9ZB6 Take a survey] and vote for what you want to learn!

=== Crowdsourced Q&A ===
Are you working on a data mining, machine learning, or statistics problem? Do you want some help? Consider sending an email to the [https://www.noisebridge.net/mailman/listinfo/ml mailing list] about it! Also consider setting up a day to come in and talk about the project you're working on and get input from other ML people.

=== About Us ===
We're a loosely-knit stochastic federation of people who like Noisebridge and like machine learning. What is machine learning? It's broad field that typically involves training computer models to solve problems. How can you participate? Join the [https://www.noisebridge.net/mailman/listinfo/ml mailing list], send an email and introduce yourself. Show up to the next meeting, share your thoughts. Participate in projects or start your own. Go to workshops, write code at workshops, learn stuff, give workshops of your own! All are welcome.

=== Talks and Workshops ===
We've given lots of workshops and talks over the past year or so, here's a few. Many of the workshops we've given previously are recurring and will be given again, especially upon request!
*[[NBML/Workshops/Intro to Machine Learning|Intro to Machine Learning]]
*[[NBML/Workshops/Brief Tour of Statistics|A Brief Tour of Statistics]]
*[[NBML/Workshops/Generalized Linear Models|Generalized Linear Models]]
*[[NBML/Workshops/Neural Nets|Neural Nets Workshop]]
*[[NBML/Workshops/Support Vector Machines|Support Vector Machines]]
*[[NBML/Workshops/Random Forests|Random Forests]]
*[[NBML/Workshops/Independent Components Analysis|Independent Components Analysis]]
*[[NBML/Workshops/Deep Nets|Deep Nets]]

=== Code and SourceForge Site ===
*We have a [http://sourceforge.net/projects/ml-noisebridge Sourceforge Project]
*We have a git repository on the project page, accessible as:
git clone git://ml-noisebridge.git.sourceforge.net/gitroot/ml-noisebridge/ml-noisebridge
*Send an email to the list if you want to become an administrator on the site to get write access to the git repo!

=== Future Talks and Topics, Ideas ===
*Restricted Boltzmann Machines (Mike S, late August)
*Deep Nets w/ Stacked Autoencoders (Mike S, September)
*Generalized Linear Models (Mike S, September/October)
*Graphical Models (Tony)
*Working with the Kinect
*Computer Vision with OpenCV

=== Mailing List ===

https://www.noisebridge.net/mailman/listinfo/ml

=== Projects ===
*[[Small Group Subproblems]]
*[[Machine Learning/Fundraising | Fundraising]]
*[[NBML_Course|Noisebridge Machine Learning Course]]
*[[Machine Learning/Kaggle Social Network Contest | Kaggle Social Network Contest]]
*[[KDD Competition 2010]]
*[[Machine Learning/Kaggle HIV | HIV]]

=== [[Machine_Learning/Datasets|Datasets and Websites]] ===
*[http://archive.ics.uci.edu/ml/ UCI Machine Learning Repository]
*[[DataSF.org]]
*[http://infochimps.com/ Infochimps]
*[http://www.face-rec.org/databases/ Face Recognition Databases]
*[http://robjhyndman.com/TSDL/ Time Series Data Library]
*[http://getthedata.org/ Data Q&A Forum]
*[http://metaoptimize.com/qa/ Metaoptimize]
*[http://www.quora.com/Machine-Learning Quora ML Page]
*[http://www.metoffice.gov.uk/research/climate/climate-monitoring/land-and-atmosphere/surface-station-records A ton of Weather Data]
*[http://mlcomp.org/ MLcomp]
**Upload your algorithm and objectively compare it's performance to other algorithms

=== [[Machine Learning/Tools | Software Tools]] ===
*[http://opencv.willowgarage.com/documentation/index.html OpenCV]
**Computer Vision Library
**Has ML component (SVM, trees, etc)
**Online tutorials [http://www.pages.drexel.edu/~nk752/tutorials.html here]
*[http://lucene.apache.org/mahout/ Mahout]
**Hadoop cluster based ML package.
*[http://www.cs.waikato.ac.nz/ml/weka/ Weka]
**a collection of data mining tools and machine learning algorithms.
*[http://moa.cs.waikato.ac.nz/ MOA (Massive Online Analysis)]
**Offshoot of weka, has all online-algorithms
*[http://scikit-learn.sourceforge.net/ scikits.learn]
**Machine learning Python package
*[http://pybrain.org PyBrain]
**Does feedforward, recurrent, SOM, deep belief nets.
*[http://www.csie.ntu.edu.tw/~cjlin/libsvm/ LIBSVM]
**c-based SVM package
*[http://pyml.sourceforge.net PyML]
*[http://mdp-toolkit.sourceforge.net/ MDP]
**Does not stand for Markov Decision Process :(
*[http://www.ailab.si/orange/ Orange]
**Strong data visualization component
*[http://jmlr.csail.mit.edu/mloss/ Journal of Machine Learning Software List]
*[[Machine Learning/VirtualBox|VirtualBox]] Virtual Box Image with Pre-installed Libraries listed here
*[http://deeplearning.net/software/theano/ Theano: Symbolic Expressions and Transparent GPU Integration]
*[http://waffles.sourceforge.net/ Waffles]
**Open source C++ set of machine learning command line tools.
*[http://rapid-i.com/content/view/181/196/ RapidMiner]
*[http://www.mrpt.org/ Mobile Robotic Programming Toolkit]
*[http://gephi.org/ Gephi]
**Graph Visualization
*[http://had.co.nz/ggplot2/ ggplot]
**Nice plotting package for R
*[http://nipy.sourceforge.net/nitime/ nitime]
**NeuroImaging in Python, has some good time series analysis stuff and multi-variate response fitting.

=== Presentations and other Materials ===
* [[Awesome Machine Learning Applications]] -- A list of cool applications of ML
* [[Hands-on Machine Learning]], a presentation [[User:jbm|jbm]] gave on 2009-01-07.
* http://www.youtube.com/user/StanfordUniversity#g/c/A89DCFA6ADACE599 Stanford Machine Learning online course videos]
* [[Media:Brief_statistics_slides.pdf]], a presentation given on statistics for the machine learning group
* [http://www.linkedin.com/groupAnswers?viewQuestionAndAnswers=&discussionID=20096092&gid=77616&trk=EML_anet_qa_ttle-0Nt79xs2RVr6JBpnsJt7dBpSBA LinkedIn] discussion on good resources for data mining and predictive analytics
* [http://www.face-rec.org/algorithms/ Face Recognition Algorithms]
* [http://www.ics.uci.edu/~welling/classnotes/classnotes.html Max Welling's ML classnotes]

=== Topics to Learn and Teach ===
[[NBML Course]] - Noisebridge Machine Learning Curriculum (work-in-progress)

[[CS229]] - The Stanford Machine learning Course @ noisebridge

*Supervised Learning
**Linear Regression
**Linear Discriminants
**Neural Nets/Radial Basis Functions
**Support Vector Machines
**Classifier Combination [http://www.cedar.buffalo.edu/~srihari/CSE555/Chap9.Part6.pdf]
**A basic decision tree builder, recursive and using entropy metrics

*Unsupervised Learning
**[[Machine_Learning/HMM|Hidden Markov Models]]
**Clustering: PCA, k-Means, Expectation-Maximization
**Graphical Modeling
**Generative Models: gaussian distribution, multinomial distributions, HMMs, Naive Bayes
**[[Machine_Learning/Deep_Belief_Networks|Deep Belief Networks & Restricted Boltzmann Machines]]

*Reinforcement Learning
**Temporal Difference Learning

*Math, Probability & Statistics
**Metric spaces and what they mean
**Fundamentals of probabilities
**Decision Theory (Bayesian)
**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]
**No-Free Lunch Theorem [http://www.cedar.buffalo.edu/~srihari/CSE555/Chap9.Part1.pdf]

*Machine Learning SDK's
** [http://opencv.willowgarage.com/documentation/index.html OpenCV] ML component (SVM, trees, etc)
**[http://lucene.apache.org/mahout/ Mahout] a Hadoop cluster based ML package.
**[http://www.cs.waikato.ac.nz/ml/weka/ Weka] a collection of data mining tools and machine learning algorithms.

*Applications
** Collective Intelligence & Recommendation Engines

=== [[Machine Learning/Meeting Notes|Meeting Notes]]===