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== History == Machine Learning groups have been perennial at Noisebridge, accumulating knowledge, projects and meeting notes since 2008. * Some of our info links may be outdated, so let us know if anything is wrong and edit the [[wiki]] as needed. === Past Teachers === *Andy McMurry === Learn about Data Science and Machine Learning === ===== Classes ===== *[https://www.coursera.org/course/ml Coursera Machine Learning Course with Andrew Ng] *[https://www.coursera.org/course/compneuro Coursera Computational Neuroscience Course with Rajesh P N Rao and Adrienne Fairhall] *[http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-867-machine-learning-fall-2006/ MIT Machine Learning Class with Tommi Jaakkola] *[http://cs229.stanford.edu/materials.html Stanford CS229] *[http://www.cs.cmu.edu/~tom/10701_sp11/lectures.shtml Carnegie Mellon Machine Learning Course with Tom Mitchell] *[http://ocw.mit.edu/courses/mathematics/18-06-linear-algebra-spring-2010/ Linear Algebra with Gilbert Strang] *[https://www.youtube.com/playlist?list=PL6Xpj9I5qXYEcOhn7TqghAJ6NAPrNmUBH Neural Networks Class with Hugo Larochelle] *[https://introtodeeplearning.com/ MIT Introduction to Deep Learning] * [https://course.fast.ai/ Practical Deep Learning for Coders - Fast.ai ] ==== Books ==== *[http://statweb.stanford.edu/~tibs/ElemStatLearn/ Elements of Statistical Learning] *[https://www.google.com/search?client=ubuntu&channel=fs&q=pattern+recognition+and+machine+learning&ie=utf-8&oe=utf-8#channel=fs&q=pattern+recognition+and+machine+learning+pdf Pattern Recognition and Machine Learning] *[https://www.google.com/search?&channel=fs&q=+Information+Theory%2C+Inference%2C+and+Learning+Algorithms.&ie=utf-8&oe=utf-8#channel=fs&q=Information+Theory%2C+Inference%2C+and+Learning+Algorithms+pdf Information Theory, Inference, and Learning Algorithms] *[http://chimera.labs.oreilly.com/books/1230000000345 Interactive Data Visualization for the Web (D3)] *[http://cran.r-project.org/doc/manuals/R-intro.pdf Introduction to R] *[http://www.dartmouth.edu/~chance/teaching_aids/books_articles/probability_book/amsbook.mac.pdf Introduction to Probability (Grinstead and Snell)] *[http://www.cis.temple.edu/~latecki/Courses/CIS2033-Spring12/A_modern_intro_probability_statistics_Dekking05.pdf Modern Introduction to Probability and Statistics (Kraaikamp and Meester)] *[http://web4.cs.ucl.ac.uk/staff/D.Barber/textbook/090310.pdf Bayesian Reasoning and Machine Learning] *[https://github.com/chandanverma07/Ebooks/blob/master/Deep%20Learning%20with%20Python%2C%20Fran%C3%A7ois%20Chollet.pdf Deep Learning with Python François Chollet] ==== Tutorials ==== *[http://nbviewer.ipython.org/github/unpingco/Python-for-Signal-Processing/tree/master/ Signal Processing IPython Notebooks] *[http://scikit-learn.org/stable/tutorial/basic/tutorial.html Introduction to ML with scikits.learn] *[http://www.sagemath.org/doc/tutorial/ Learn how to use SAGE] *[https://skillcombo.com/topic/machine-learning/ Online Machine Learning Courses] ==== Noisebridge ML Class Slides ==== *[[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 === 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!] *[http://www.sipri.org/databases SIPRI Social Databases] **Wealth of information on international arms transfers and peace missions. *[http://aws.amazon.com/publicdatasets/ Amazon AWS Public Datasets] *[http://www.prio.no/Data/Armed-Conflict/ UCDP/PRIO Armed Conflict Datasets] *[https://opendata.socrata.com/browse Socrata Government Datasets] *[http://us-city.census.okfn.org/ US City Census Data] *[http://webscope.sandbox.yahoo.com/catalog.php Yahoo Labs Datasets] === 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://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://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 *[http://statsmodels.sourceforge.net/ statsmodels] **Regression, time series analysis, statistics stuff for python *[https://github.com/JohnLangford/vowpal_wabbit/wiki Vowpal Wabbit] **"Intrinsically Fast" implementation of gradient descent for large datasets *[http://www.shogun-toolbox.org/ Shogun] **Fast implementations of SVMs *[http://www.mlpack.org/ MLPACK] **High performance scalable ML Library *[http://www.torch.ch/ Torch] **MATLAB-like environment for state-of-the art ML libraries written in LUA ==== Deep Nets ==== *[http://deeplearning.net/software/theano/ Theano] **Symbolic Expressions and Transparent GPU Integration *[http://caffe.berkeleyvision.org/ Caffe] **Convolutional Neural Networks on GPU *[https://code.google.com/p/neurolab/ Neurolab] **Has support for recurrent neural nets ==== Online ML ==== *[http://moa.cs.waikato.ac.nz/ MOA (Massive Online Analysis)] **Offshoot of weka, has all online-algorithms *[http://jubat.us/en/ Jubatus] **Distributed Online ML *[http://dogma.sourceforge.net/ DOGMA] **MATLAB-based online learning stuff *[http://code.google.com/p/libol/ libol] *[http://code.google.com/p/oll/ oll] *[http://code.google.com/p/scw-learning/ scw-learning] ==== Graphical Models ==== *[http://www.mrc-bsu.cam.ac.uk/bugs/ BUGS] **MCMC for Bayesian Models *[http://mcmc-jags.sourceforge.net/ JAGS] **Hierarchical Bayesian Models *[http://mc-stan.org/ Stan] **A graphical model compiler *[https://github.com/kutschkem/Jayes Jayes] **Bayesian networks in Java *[http://tops.sourceforge.net/ ToPS] **Probabilistic models of sequences *[http://pymc-devs.github.io/pymc/ PyMC] **Bayesian Models in Python ==== Text Stuff ==== *[http://www.crummy.com/software/BeautifulSoup/ Beautiful Soup] **Screen-scraping tools *[http://www.mlsec.org/sally/ SALLY] **Tool for embedding strings into vector spaces *[http://radimrehurek.com/gensim/ Gensim] **Topic modeling ==== Collaborative Filtering ==== *[http://prea.gatech.edu/ PREA] **Personalized Recommendation Algorithms Toolkit *[http://svdfeature.apexlab.org/wiki/Main_Page SVDFeature] **Collaborative Filtering and Ranking Toolkit ==== 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] *[http://drwn.anu.edu.au/ DARWIN] **Generic C++ ML and Computer Vision Library *[http://sourceforge.net/projects/petavision/ PetaVision] **Developing a real-time, full-scale model of the primate visual cortex. ==== 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://www.fon.hum.uva.nl/praat/ PRAAT] **Speech analysis toolkit *[http://ofer.sci.ccny.cuny.edu/sound_analysis_pro Sound Analysis Pro] **Tool for analyzing animal sounds *[http://luscinia.sourceforge.net/ Luscinia] **Software for archiving, measuring, and analyzing bioacoustic data *[http://wiki.python.org/moin/PythonInMusic List of Sound Tools for Python] *[http://jasperproject.github.io/ Jasper] **Voice-control anything! ==== 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 *[http://cytoscape.github.io/cytoscape.js/ Cytoscape] **A JavaScript graph library for analysis and visualisation *[https://plot.ly/ plot.ly] **Web-based plotting *[http://chimera.labs.oreilly.com/books/1230000000345/ch02.html D3 Ebook] **Has a good list of HTML/CSS/Javascript data visualization tools. *[https://plot.ly/ plotly] **Python plotting tool ==== 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 ==== Database Stuff ==== *[http://madlib.net/ MADlib] **Machine learning algorithms for in-database data *[http://www.joyent.com/products/manta Manta] **Distributed object storage ==== Neural Simulation ==== *[http://nengo.ca/ Nengo] ==== 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 [[Category:Events]] [[Category:Projects]]
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