https://www.noisebridge.net/api.php?action=feedcontributions&user=124.6.181.171&feedformat=atomNoisebridge - User contributions [en]2024-03-28T22:16:36ZUser contributionsMediaWiki 1.39.4https://www.noisebridge.net/index.php?title=Noisebridge_Vision&diff=24920Noisebridge Vision2012-03-31T10:22:21Z<p>124.6.181.171: </p>
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<div>__NOEDITSECTION__[[Image:Noisebridge_soldering.jpg|thumb|right|180px|Learning by doing!]]<br />
[[Image:Evocell_ecoli15.png|thumb|right|180px|Do cool stuff!]]<br />
[[Image:Noisebridge_at_night.jpg|thumb|right|180px|Interact with interesting people!]]<br />
Noisebridge is a space for sharing, creation, collaboration, research, development, mentoring, and of course, learning. Noisebridge is also more than a physical space, it's a community with roots extending around the world.<br />
<br />
<blockquote><br />
For we're excellent to each other here<br /><br />
We rarely ever block<br /><br />
We value tools over pre-emptive rules<br /><br />
And spurn the key and the lock.<br /><br />
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; &mdash; Danny O'Brien, 2010-11-09 general meeting notes<br />
</blockquote><br />
<br />
''We make stuff. So can you.''<br />
<br />
<br />
== The Idea ==<br />
<br />
Noisebridge is an educational non-profit corporation, 501(c)3 public charity status.<br />
<br />
We provide infrastructure and collaboration opportunities for people interested in programming, hardware hacking, physics, chemistry, mathematics, photography, security, robotics, all kinds of art, and, of course, technology. Through talks, workshops, and [[projects]] we encourage knowledge exchange, learning, and mentoring.<br />
<br />
As a space for artistic collaboration and experimentation, we are open to all types of art - with a special emphasis on the crossover of art and technology. From hardware labs to electronics, cooking, photography, and sound labs, anything that's creative is welcome.<br />
<br />
Many interesting things are happening at all times. Sharing is essential to making this work. We believe in starting from a point of respect and trust. We believe it builds a safe community and that this will foster innovation and creation. <br />
Our code of conduct is "Be excellent to each other".<br />
<br />
Leadership is taken by individual members for specific projects. We call this "sudo leadership" after the *nix command sudo which allows a regular user to do one root-level, or superuser, task. In other words, if you want Noisebridge to do something, start doing it.<br />
<br />
Here's some paraphrasing from our bylaws:<br />
Through talks, classes, workshops, collaborative projects, and other activities, we want to encourage research, knowledge exchange, learning, and mentoring in a safe, clean space. We provide educational spaces for teaching practical skills and theory of technology, science, and art. We provide work space, storage, and other resources for projects related to art, science, and technology that will benefit the individual members' personal growth in their fields of interest, encouraging the individual members to share their projects and knowledge for the betterment of society through art, science and technology. We create, learn, and teach, individually and as a group, inviting members of the community in the San Francisco Bay area and the world. We develop, support the development of, and provide resources for the development of free and open source software and hardware for the benefit of society. We promote collaboration across disciplines for the benefit of cultural, charitable, and scientific causes.<br />
<br />
== Tripartite Pillars ==<br />
<br />
===Excellence===<br />
'''Be excellent to each other''' is the guiding principle of Noisebridge. Wikipedia uses [http://meta.wikimedia.org/wiki/Don't_be_a_dick a somewhat similar rule], which they call "''the fundamental rule of all social spaces. Every other policy for getting along is a special case of it.''" Unlike Wikipedia, Noisebridge takes a positive approach, and avoids the practice of officially enumerating the myriad potential special cases; "be excellent" is enough.<br />
<br />
===Consensus===<br />
We make official Noisebridge decisions by consensus, which means the willing consent of all of our members. Decisions are made at our [[:Category:Meeting_Notes|weekly meetings]], and items proposed for consensus are announced at least a week in advance to give everyone time to hear about them. Members may block by proxy if they are unable to attend or if they wish to block anonymously.<br />
<br />
More information on the [[Consensus Process]].<br />
<br />
===Do-ocracy===<br />
Doing excellent stuff at Noisebridge does not require permission or an official consensus decision. If you're uncertain about the excellence of something you want to do, you should ask someone else what they think.<br />
<br />
== Testimonials ==<br />
<br />
[[Testimonials | Why people love Noisebridge]]<br />
<br />
* Smart creative people, welcoming community, friendly to newbies <br />
* Equipment, tools, books, materials, and the space itself<br />
* Its culture of free, open, accessible, DIY awesomeness<br />
<br />
<br />
== Financing it ==<br />
<br />
We self-finance through membership fees ($80 per member/month with $40 "starving hacker" rate), beverage sales, and parties, the way European hacker spaces do it. We also welcome one-time or recurring [[Donate_or_Pay_Dues|donations]] from members and non-members alike. Donations and sponsorships will accompany renovation and equipment purchase. Within the first 24 hours of renting a space, we raised over $10,000 for a cool location and meaningful projects. Within our first month, we've nearly become cash flow positive from membership <span class="plainlinks">[http://www.andrewflusche.com/services/stafford-dui-lawyer/<span style="color:black;font-weight:normal; text-decoration:none!important; background:none!important; text-decoration:none;">Stafford DUI lawyer</span>] dues alone. Further discussion is happening on the [[Finances]] wiki page. <br />
<br />
== Inspiration ==<br />
Noisebridge is inspired by similar European clubs like [http://en.wikipedia.org/wiki/Metalab Metalab] of Vienna, [http://en.wikipedia.org/wiki/C-base CBase] of Berlin, [http://www.mi2.hr/ MAMA] of Zagreb, and [http://en.wikipedia.org/wiki/ASCII_%28squat%29 ASCII] of Amsterdam. Many other clubs of a similar stripe can be found at [http://hacklabs.org/index_en.php Hacklabs] and [http://hackerspaces.org/wiki/List_of_Hacker_Spaces Hackerspaces dot Org]. It would not be out of the question to consider Noisebridge a possible [http://events.ccc.de/2007/09/27/say-hello-to-bitkanonecccde/ San Francisco Chaostreff]. Noisebridge is a hacker space and community that shares a [http://en.wikipedia.org/wiki/Dorkbot Dorkbot]-like ethic, and indeed, many of the members of Noisebridge are long-time Dorkbotters.<br />
<br />
== The Name ==<br />
A "noise bridge" performs useful services by injecting noise into a system. Such a device is often used in RF electronics.<br />
<br />
== The Space ==<br />
Read some [[oral histories]] from members, or add your own!<br />
<br />
=See Also=<br />
*[[Community Standards]]</div>124.6.181.171https://www.noisebridge.net/index.php?title=Machine_Learning&diff=23436Machine Learning2012-02-15T08:42:00Z<p>124.6.181.171: </p>
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<div>=== Next Meeting===<br />
<br />
*When: TBD (Some time in March)<br />
*Where: 2169 Mission St. (back corner, Church or Turing classroom, undecided)<br />
*Topic: Hack Campaign Finance Data<br />
*Details: Brainstorming session to figure out what to do with [http://data.influenceexplorer.com/bulk/ campaign finance data]<br />
*Who: Mike S<br />
<br />
=== Take the Noisebridge ML Survey ===<br />
[http://www.surveymonkey.com/s/W2T9ZB6 Take a survey] and vote for what you want to learn!<br />
<br />
=== Crowdsourced Q&A ===<br />
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.<br />
<br />
=== About Us ===<br />
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.<br />
<br />
=== Talks and Workshops ===<br />
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!<br />
*[[NBML/Workshops/Intro to Machine Learning|Intro to Machine Learning]]<br />
*[[NBML/Workshops/Brief Tour of Statistics|A Brief Tour of Statistics]]<br />
*[[NBML/Workshops/Generalized Linear Models|Generalized Linear Models]]<br />
*[[NBML/Workshops/Neural Nets|Neural Nets Workshop]]<br />
*[[NBML/Workshops/Support Vector Machines|Support Vector Machines]]<br />
*[[NBML/Workshops/Random Forests|Random Forests]]<br />
*[[NBML/Workshops/Independent Components Analysis|Independent Components Analysis]]<br />
*[[NBML/Workshops/Deep Nets|Deep Nets]]<br />
<br />
=== Code and SourceForge Site ===<br />
*We have a [http://sourceforge.net/projects/ml-noisebridge Sourceforge Project]<br />
*We have a git repository on the project page, accessible as:<br />
git clone git://ml-noisebridge.git.sourceforge.net/gitroot/ml-noisebridge/ml-noisebridge<br />
*Send an email to the list if you want to become an administrator on the site to get write access to the git repo!<br />
<br />
=== Future Talks and Topics, Ideas ===<br />
*Random Forests in R (Mike S, 1/26)<br />
*Restricted Boltzmann Machines (Mike S, some day)<br />
*Analyzing brain cells (Mike S)<br />
*Deep Nets w/ Stacked Autoencoders (Mike S, some day)<br />
*Generalized Linear Models (Mike S, Erin L? some day)<br />
*Graphical Models<br />
*Working with the Kinect<br />
*Computer Vision with OpenCV<br />
<br />
=== Mailing List ===<br />
<br />
https://www.noisebridge.net/mailman/listinfo/ml<br />
<br />
=== Projects ===<br />
*[[Small Group Subproblems]]<br />
*[[Machine Learning/Fundraising | Fundraising]]<br />
*[[NBML_Course|Noisebridge Machine Learning Course]]<br />
*[[Machine Learning/Kaggle Social Network Contest | Kaggle Social Network Contest]]<br />
*[[KDD Competition 2010]]<br />
*[[Machine Learning/Kaggle HIV | HIV]]<br />
<br />
=== [[Machine_Learning/Datasets|Datasets and Websites]] ===<br />
*[http://archive.ics.uci.edu/ml/ UCI Machine Learning Repository]<br />
*[[DataSF.org]]<br />
*[http://infochimps.com/ Infochimps]<br />
*[http://www.face-rec.org/databases/ Face Recognition Databases]<br />
*[http://robjhyndman.com/TSDL/ Time Series Data Library]<br />
*[http://getthedata.org/ Data Q&A Forum]<br />
*[http://metaoptimize.com/qa/ Metaoptimize]<br />
*[http://www.quora.com/Machine-Learning Quora ML Page]<br />
*[http://www.metoffice.gov.uk/research/climate/climate-monitoring/land-and-atmosphere/surface-station-records A ton of Weather Data]<br />
*[http://mlcomp.org/ MLcomp]<br />
**Upload your algorithm and objectively compare it's performance to other algorithms<br />
<br />
=== [[Machine Learning/Tools | Software Tools]] ===<br />
*[http://opencv.willowgarage.com/documentation/index.html OpenCV]<br />
**Computer Vision Library<br />
**Has ML component (SVM, trees, etc)<br />
**Online tutorials [http://www.pages.drexel.edu/~nk752/tutorials.html here]<br />
*[http://lucene.apache.org/mahout/ Mahout]<br />
**Hadoop cluster based ML package.<br />
*[http://www.cs.waikato.ac.nz/ml/weka/ Weka]<br />
**a collection of data mining tools and machine learning algorithms.<br />
*[http://moa.cs.waikato.ac.nz/ MOA (Massive Online Analysis)]<br />
**Offshoot of weka, has all online-algorithms<br />
*[http://scikit-learn.sourceforge.net/ scikits.learn]<br />
**Machine learning Python package<br />
*[http://pypi.python.org/pypi/scikits.statsmodels scikits.statsmodels]<br />
**Statistical models to go with scipy<br />
*[http://pybrain.org PyBrain]<br />
**Does feedforward, recurrent, SOM, deep belief nets.<br />
*[http://www.csie.ntu.edu.tw/~cjlin/libsvm/ LIBSVM]<br />
**c-based SVM package<br />
*[http://pyml.sourceforge.net PyML]<br />
*[http://mdp-toolkit.sourceforge.net/ MDP]<br />
**Does not stand for Markov Decision Process :(<br />
*[http://www.ailab.si/orange/ Orange]<br />
**Strong data visualization component<br />
*[http://jmlr.csail.mit.edu/mloss/ Journal of Machine Learning Software List]<br />
*[[Machine Learning/VirtualBox|VirtualBox]] Virtual Box Image with Pre-installed Libraries listed here<br />
*[http://deeplearning.net/software/theano/ Theano: Symbolic Expressions and Transparent GPU Integration]<br />
*[http://sympy.org sympy] Does symbolic math<br />
*[http://waffles.sourceforge.net/ Waffles]<br />
**Open source C++ set of machine learning command line tools.<br />
*[http://rapid-i.com/content/view/181/196/ RapidMiner]<br />
*[http://www.mrpt.org/ Mobile Robotic Programming Toolkit]<br />
*[http://gephi.org/ Gephi]<br />
**Graph Visualization<br />
*[http://had.co.nz/ggplot2/ ggplot]<br />
**Nice plotting package for R<br />
*[http://nipy.sourceforge.net/nitime/ nitime]<br />
**NeuroImaging in Python, has some good time series analysis stuff and multi-variate response fitting.<br />
*[http://web.mit.edu/star/cluster/ STAR: Cluster]<br />
**Easily build your own Python computing cluster on Amazon EC2<br />
*[http://code.enthought.com/projects/mayavi/ MayaVi2]<br />
**3D Scientific Data Visualization<br />
<br />
=== Presentations and other Materials ===<br />
* [[Awesome Machine Learning Applications]] -- A list of cool applications of ML<br />
* [[Hands-on Machine Learning]], a presentation [[User:jbm|jbm]] gave on 2009-01-07.<br />
* http://www.youtube.com/user/StanfordUniversity#g/c/A89DCFA6ADACE599 Stanford Machine Learning online course videos]<br />
* [[Media:Brief_statistics_slides.pdf]], a presentation given on statistics for the machine learning group<br />
* [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<br />
* [http://www.face-rec.org/algorithms/ Face Recognition Algorithms]<br />
* [http://www.ics.uci.edu/~welling/classnotes/classnotes.html Max Welling's ML classnotes]<br />
<br />
=== Topics to Learn and Teach ===<br />
[[NBML Course]] - Noisebridge Machine Learning Curriculum (work-in-progress)<br />
<br />
[[CS229]] - The Stanford Machine learning Course @ noisebridge<br />
<br />
*Supervised Learning<br />
**Linear Regression<br />
**Linear Discriminants<br />
**Neural Nets/Radial Basis Functions<br />
**Support Vector Machines<br />
**Classifier Combination [http://www.cedar.buffalo.edu/~srihari/CSE555/Chap9.Part6.pdf]<br />
**A basic decision tree builder, recursive and using entropy metrics<br />
<br />
*Unsupervised Learning<br />
**[[Machine_Learning/HMM|Hidden Markov Models]]<br />
**Clustering: PCA, k-Means, Expectation-Maximization<br />
**Graphical Modeling<br />
**Generative Models: gaussian distribution, multinomial distributions, HMMs, Naive Bayes<br />
**[[Machine_Learning/Deep_Belief_Networks|Deep Belief Networks & Restricted Boltzmann Machines]]<br />
<br />
*Reinforcement Learning<br />
**Temporal Difference Learning<br />
<br />
*Math, Probability & Statistics<br />
**Metric spaces and what they mean<br />
**Fundamentals of probabilities<br />
**Decision Theory (Bayesian)<br />
**Maximum Likelihood<br />
**Bias/Variance Tradeoff, VC Dimension<br />
**Bagging, Bootstrap, Jacknife [http://www.cedar.buffalo.edu/~srihari/CSE555/Chap9.Part3.pdf]<br />
**Information Theory: Entropy, Mutual Information, Gaussian Channels<br />
**Estimation of Misclassification [http://www.cedar.buffalo.edu/~srihari/CSE555/Chap9.Part5.pdf]<br />
**No-Free Lunch Theorem [http://www.cedar.buffalo.edu/~srihari/CSE555/Chap9.Part1.pdf]<br />
<br />
*Machine Learning SDK's<br />
** [http://opencv.willowgarage.com/documentation/index.html OpenCV] ML component (SVM, trees, etc)<br />
**[http://lucene.apache.org/mahout/ Mahout] a Hadoop cluster based ML package.<br />
**[http://www.cs.waikato.ac.nz/ml/weka/ Weka] a collection of data mining tools and machine learning algorithms.<br />
<br />
*Applications<br />
** Collective Intelligence & Recommendation Engines<br />
<br />
=== [[Machine Learning/Meeting Notes|Meeting Notes]]===</div>124.6.181.171