Noisebridge - User contributions [en]

Machine Learning

2012-05-17T19:14:49Z

128.32.52.248:

=== Next Meeting===

*When: 5/24/2012 7:30 PM
*Where: 2169 Mission St. (back NE corner, Church classroom)
*Topic: Data-mining the noisebridge-discuss mailing list
*Details: We'll talk about work by Jared D to data-mine noisebridge-discuss, and popular text methods like TF*IDF and LDA.
*Who: Jared D, Mike S

=== 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 <span class="plainlinks">[http://www.andrewflusche.com/services/spotsylvania-reckless-driving-defense/<span style="color:black;font-weight:normal; text-decoration:none!important; background:none!important; text-decoration:none;">Spotsylvania reckless driving</span>] 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 ===
*Random Forests in R
*Restricted Boltzmann Machines (Mike S, some day)
*Analyzing brain cells (Mike S)
*Deep Nets w/ Stacked Autoencoders (Mike S, some day)
*Generalized Linear Models (Mike S, Erin L? some day)
*Graphical Models
*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
*[http://www.ntis.gov/products/ssa-dmf.aspx Social Security Death Master File!]

=== Software Tools ===

==== Generic ML Libraries ====
*[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://pypi.python.org/pypi/scikits.statsmodels scikits.statsmodels]
**Statistical models to go with scipy
*[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]
**Modular framework, has lots of stuff!
*[[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://sympy.org sympy] Does symbolic math
*[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://nipy.sourceforge.net/nitime/ nitime]
**NeuroImaging in Python, has some good time series analysis stuff and multi-variate response fitting.
*[http://pandas.pydata.org/ Pandas]
**Data analysis workflow in python
*[http://www.pytables.org/moin PyTables]
**Adds querying capabilities to HDF5 files

==== Computer Vision ====
*[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]

==== Audio Processing ====
*[http://tlecomte.github.com/friture/ Friture]
**Real-time spectrogram generation
*[http://code.google.com/p/pyo/ pyo]
**Real-time audio signal processing
*[https://github.com/jsawruk/pymir PYMir]
**A library for reading mp3's into python, and doing analysis
*[http://wiki.python.org/moin/PythonInMusic List of Sound Tools for Python]

==== Data Visualization ====
*[http://www.ailab.si/orange/ Orange]
**Strong data visualization component
*[http://gephi.org/ Gephi]
**Graph Visualization
*[http://had.co.nz/ggplot2/ ggplot]
**Nice plotting package for R
*[http://code.enthought.com/projects/mayavi/ MayaVi2]
**3D Scientific Data Visualization

==== Cluster Computing ====
*[http://lucene.apache.org/mahout/ Mahout]
**Hadoop cluster based ML package.
*[http://web.mit.edu/star/cluster/ STAR: Cluster]
**Easily build your own Python computing cluster on Amazon EC2

==== Other ====
*[http://jmlr.csail.mit.edu/mloss/ Journal of Machine Learning Software List]

=== 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

2011-11-16T06:01:13Z

128.32.52.248:

=== Next Meeting===

Note: there is currently a study group for the [http://www.ml-class.org/ Stanford ML Course] that meets up every Wednesday @ 7:30pm in the Church room, Monday meetups will be reserved for special presentations and announced here and on the [https://www.noisebridge.net/mailman/listinfo/ml mailing list].

*When: Wednesday, 11/16/2011 @ 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

2011-09-08T22:46:40Z

128.32.52.248: /* Next Meeting */

=== Next Meeting===

*When: Monday, 9/12/2011 @ 7:00-8:30pm
*Where: 2169 Mission St. (back corner, Church classroom)
*Topic: Introduction to Machine Learning and R
*Details: A Survey of what "Machine Learning" means, and the R programming environment.

=== 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]

=== 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

2011-08-30T22:56:29Z

128.32.52.248:

=== 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.

=== Next Meeting===

*When: Monday, 8/29/2011 @ 7:00-8:30pm
*Where: 2169 Mission St. (back corner, Church classroom)
*Topic: Discussions over Beer
*Details: What do you want to get out of the ML meetups? Come in and share your thoughts!

=== 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]

=== 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]]===

NewChurchProjector

2011-08-22T21:28:12Z

128.32.52.248:

No more need to contribute, money has been paid back. Thank you!!!

NewChurchProjector

2011-08-17T19:50:03Z

128.32.52.248:

We got a new projector for the Church room! It cost $100 on Craig's list. I'd (Mike S) like to get that money back, as I'm broke!

I'd very much appreciate if you could pledge a bit of money towards the projector that would go towards paying me back. To keep things honest, if you could contact me (mike@mindmech.com) and leave your name and a donation amount here, that would seem ideal.

Pledges
--------

*Krishna - $50 (PAID)
*[[User:Malaclyps]] - $30 (PAID)
*Rachel M - $20 (PENDING)