Machine Learning: Difference between revisions
Jump to navigation
Jump to search
Mschachter (talk | contribs) mNo edit summary |
m (Correct link, to User:Culteejen*) |
||
(109 intermediate revisions by 23 users not shown) | |||
Line 1: | Line 1: | ||
{{ai}} | |||
= | {{headerbox}}<font size=5>AI and reinforcement learning meetup at Noisebridge Wednesdays at 8pm.</font> | ||
[ | *[https://www.meetup.com/noisebridge/events/kpsdrsyccqblb/ AI and Reinforcement Learning Meetup page] | ||
*'''WHEN:''' Wednesdays at 8:00pm | |||
*'''WHERE:''' 272 Capp St. (Church classroom) | |||
*'''WHO:''' Anyone interested in learning about artificial intelligence, machine learning and related topics. | |||
*'''CHANNELS:''' Join the [https://www.noisebridge.net/mailman/listinfo/ml|https://www.noisebridge.net/mailman/listinfo/ml] mailing list. #ai on [[Discord]] and [[Slack]] | |||
* '''MAINTAINERS:''' TJ/[[User:Culteejen]], [[User:Ryan_L]] | |||
* '''NOTES:''' [[Machine Learning/Meeting Notes|Meeting Notes]] | |||
{{boxend}} | |||
=== | === Join the Mailing List === | ||
=== | https://www.noisebridge.net/mailman/listinfo/ml | ||
== 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* Some of our info links may be outdated, so let us know if anything is wrong and edit the [[wiki]] as needed.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/Intro to Machine Learning|Intro to Machine Learning]] | ||
*[[NBML/Workshops/Brief Tour of Statistics|A Brief Tour of Statistics]] | *[[NBML/Workshops/Brief Tour of Statistics|A Brief Tour of Statistics]] | ||
Line 42: | Line 78: | ||
*Working with the Kinect | *Working with the Kinect | ||
*Computer Vision with OpenCV | *Computer Vision with OpenCV | ||
=== Projects === | === Projects === | ||
Line 68: | Line 100: | ||
**Upload your algorithm and objectively compare it's performance to other algorithms | **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.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 === | === Software Tools === | ||
Line 74: | Line 113: | ||
*[http://www.cs.waikato.ac.nz/ml/weka/ Weka] | *[http://www.cs.waikato.ac.nz/ml/weka/ Weka] | ||
**a collection of data mining tools and machine learning algorithms. | **a collection of data mining tools and machine learning algorithms. | ||
*[http://scikit-learn.sourceforge.net/ scikits.learn] | *[http://scikit-learn.sourceforge.net/ scikits.learn] | ||
**Machine learning Python package | **Machine learning Python package | ||
Line 88: | Line 125: | ||
**Modular framework, has lots of stuff! | **Modular framework, has lots of stuff! | ||
*[[Machine Learning/VirtualBox|VirtualBox]] Virtual Box Image with Pre-installed Libraries listed here | *[[Machine Learning/VirtualBox|VirtualBox]] Virtual Box Image with Pre-installed Libraries listed here | ||
*[http://sympy.org sympy] Does symbolic math | *[http://sympy.org sympy] Does symbolic math | ||
*[http://waffles.sourceforge.net/ Waffles] | *[http://waffles.sourceforge.net/ Waffles] | ||
Line 100: | Line 136: | ||
*[http://www.pytables.org/moin PyTables] | *[http://www.pytables.org/moin PyTables] | ||
**Adds querying capabilities to HDF5 files | **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 ==== | ==== Computer Vision ==== | ||
Line 106: | Line 199: | ||
**Has ML component (SVM, trees, etc) | **Has ML component (SVM, trees, etc) | ||
**Online tutorials [http://www.pages.drexel.edu/~nk752/tutorials.html here] | **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 ==== | ==== Audio Processing ==== | ||
Line 114: | Line 211: | ||
*[https://github.com/jsawruk/pymir PYMir] | *[https://github.com/jsawruk/pymir PYMir] | ||
**A library for reading mp3's into python, and doing analysis | **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://wiki.python.org/moin/PythonInMusic List of Sound Tools for Python] | ||
*[http://jasperproject.github.io/ Jasper] | |||
**Voice-control anything! | |||
==== Data Visualization ==== | ==== Data Visualization ==== | ||
Line 125: | Line 230: | ||
*[http://code.enthought.com/projects/mayavi/ MayaVi2] | *[http://code.enthought.com/projects/mayavi/ MayaVi2] | ||
**3D Scientific Data Visualization | **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 ==== | ==== Cluster Computing ==== | ||
*[http://lucene.apache.org/mahout/ Mahout] | *[http://lucene.apache.org/mahout/ Mahout] | ||
Line 131: | Line 243: | ||
*[http://web.mit.edu/star/cluster/ STAR: Cluster] | *[http://web.mit.edu/star/cluster/ STAR: Cluster] | ||
**Easily build your own Python computing cluster on Amazon EC2 | **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 ==== | ==== Other ==== | ||
Line 186: | Line 307: | ||
** Collective Intelligence & Recommendation Engines | ** Collective Intelligence & Recommendation Engines | ||
[[Category:Events]] | |||
[[Category:Projects]] |
Latest revision as of 18:03, 29 November 2023
Noisebridge | About | Visit | 272 | Manual | Contact | Guilds | Resources | Events | Projects | 5MoF | Meetings | Donate | (Edit) |
Guilds | Meta | Code | Electronics | Fabrication | Games | Sewing | Music | AI | Neuro | Philosophy | Funding | Art | Security | Ham | Brew | (Edit) |
AI | Machine Learning | Botbridge | DreamTeam | ML Tools | (Edit) |
AI and reinforcement learning meetup at Noisebridge Wednesdays at 8pm.
|
Join the Mailing List[edit]
https://www.noisebridge.net/mailman/listinfo/ml
History[edit]
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[edit]
- Andy McMurry
Learn about Data Science and Machine Learning[edit]
Classes[edit]
- Coursera Machine Learning Course with Andrew Ng
- Coursera Computational Neuroscience Course with Rajesh P N Rao and Adrienne Fairhall
- MIT Machine Learning Class with Tommi Jaakkola
- Stanford CS229
- Carnegie Mellon Machine Learning Course with Tom Mitchell
- Linear Algebra with Gilbert Strang
- Neural Networks Class with Hugo Larochelle
- MIT Introduction to Deep Learning
- Practical Deep Learning for Coders - Fast.ai
Books[edit]
- Elements of Statistical Learning
- Pattern Recognition and Machine Learning
- Information Theory, Inference, and Learning Algorithms
- Interactive Data Visualization for the Web (D3)
- Introduction to R
- Introduction to Probability (Grinstead and Snell)
- Modern Introduction to Probability and Statistics (Kraaikamp and Meester)
- Bayesian Reasoning and Machine Learning
- Deep Learning with Python François Chollet
Tutorials[edit]
- Signal Processing IPython Notebooks
- Introduction to ML with scikits.learn
- Learn how to use SAGE
- Online Machine Learning Courses
Noisebridge ML Class Slides[edit]
- Intro to Machine Learning
- A Brief Tour of Statistics
- Generalized Linear Models
- Neural Nets Workshop
- Support Vector Machines
- Random Forests
- Independent Components Analysis
- Deep Nets
Code and SourceForge Site[edit]
- We have a 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[edit]
- 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[edit]
- Small Group Subproblems
- Fundraising
- Noisebridge Machine Learning Course
- Kaggle Social Network Contest
- KDD Competition 2010
- HIV
Datasets and Websites[edit]
- UCI Machine Learning Repository
- DataSF.org
- Infochimps
- Face Recognition Databases
- Time Series Data Library
- Data Q&A Forum
- Metaoptimize
- Quora ML Page
- A ton of Weather Data
- MLcomp
- Upload your algorithm and objectively compare it's performance to other algorithms
- Social Security Death Master File!
- SIPRI Social Databases
- Wealth of information on international arms transfers and peace missions.
- Amazon AWS Public Datasets
- UCDP/PRIO Armed Conflict Datasets
- Socrata Government Datasets
- US City Census Data
- Yahoo Labs Datasets
Software Tools[edit]
Generic ML Libraries[edit]
- Weka
- a collection of data mining tools and machine learning algorithms.
- scikits.learn
- Machine learning Python package
- scikits.statsmodels
- Statistical models to go with scipy
- PyBrain
- Does feedforward, recurrent, SOM, deep belief nets.
- LIBSVM
- c-based SVM package
- PyML
- MDP
- Modular framework, has lots of stuff!
- VirtualBox Virtual Box Image with Pre-installed Libraries listed here
- sympy Does symbolic math
- Waffles
- Open source C++ set of machine learning command line tools.
- RapidMiner
- Mobile Robotic Programming Toolkit
- nitime
- NeuroImaging in Python, has some good time series analysis stuff and multi-variate response fitting.
- Pandas
- Data analysis workflow in python
- PyTables
- Adds querying capabilities to HDF5 files
- statsmodels
- Regression, time series analysis, statistics stuff for python
- Vowpal Wabbit
- "Intrinsically Fast" implementation of gradient descent for large datasets
- Shogun
- Fast implementations of SVMs
- MLPACK
- High performance scalable ML Library
- Torch
- MATLAB-like environment for state-of-the art ML libraries written in LUA
Deep Nets[edit]
- Theano
- Symbolic Expressions and Transparent GPU Integration
- Caffe
- Convolutional Neural Networks on GPU
- Neurolab
- Has support for recurrent neural nets
Online ML[edit]
- MOA (Massive Online Analysis)
- Offshoot of weka, has all online-algorithms
- Jubatus
- Distributed Online ML
- DOGMA
- MATLAB-based online learning stuff
- libol
- oll
- scw-learning
Graphical Models[edit]
- BUGS
- MCMC for Bayesian Models
- JAGS
- Hierarchical Bayesian Models
- Stan
- A graphical model compiler
- Jayes
- Bayesian networks in Java
- ToPS
- Probabilistic models of sequences
- PyMC
- Bayesian Models in Python
Text Stuff[edit]
- Beautiful Soup
- Screen-scraping tools
- SALLY
- Tool for embedding strings into vector spaces
- Gensim
- Topic modeling
Collaborative Filtering[edit]
- PREA
- Personalized Recommendation Algorithms Toolkit
- SVDFeature
- Collaborative Filtering and Ranking Toolkit
Computer Vision[edit]
- OpenCV
- Computer Vision Library
- Has ML component (SVM, trees, etc)
- Online tutorials here
- DARWIN
- Generic C++ ML and Computer Vision Library
- PetaVision
- Developing a real-time, full-scale model of the primate visual cortex.
Audio Processing[edit]
- Friture
- Real-time spectrogram generation
- pyo
- Real-time audio signal processing
- PYMir
- A library for reading mp3's into python, and doing analysis
- PRAAT
- Speech analysis toolkit
- Sound Analysis Pro
- Tool for analyzing animal sounds
- Luscinia
- Software for archiving, measuring, and analyzing bioacoustic data
- List of Sound Tools for Python
- Jasper
- Voice-control anything!
Data Visualization[edit]
- Orange
- Strong data visualization component
- Gephi
- Graph Visualization
- ggplot
- Nice plotting package for R
- MayaVi2
- 3D Scientific Data Visualization
- Cytoscape
- A JavaScript graph library for analysis and visualisation
- plot.ly
- Web-based plotting
- D3 Ebook
- Has a good list of HTML/CSS/Javascript data visualization tools.
- plotly
- Python plotting tool
Cluster Computing[edit]
- Mahout
- Hadoop cluster based ML package.
- STAR: Cluster
- Easily build your own Python computing cluster on Amazon EC2
Database Stuff[edit]
Neural Simulation[edit]
Other[edit]
Presentations and other Materials[edit]
- Awesome Machine Learning Applications -- A list of cool applications of ML
- Hands-on Machine Learning, a presentation 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
- LinkedIn discussion on good resources for data mining and predictive analytics
- Face Recognition Algorithms
- Max Welling's ML classnotes
Topics to Learn and Teach[edit]
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 [2]
- A basic decision tree builder, recursive and using entropy metrics
- Unsupervised Learning
- Hidden Markov Models
- Clustering: PCA, k-Means, Expectation-Maximization
- Graphical Modeling
- Generative Models: gaussian distribution, multinomial distributions, HMMs, Naive Bayes
- 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 [3]
- Information Theory: Entropy, Mutual Information, Gaussian Channels
- Estimation of Misclassification [4]
- No-Free Lunch Theorem [5]
- Machine Learning SDK's
- Applications
- Collective Intelligence & Recommendation Engines