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{{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 === | === Join the Mailing List === | ||
https://www.noisebridge.net/mailman/listinfo/ml | 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* Some of our info links may be outdated, so let us know if anything is wrong and edit the [[wiki]] as needed.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 === | === Learn about Data Science and Machine Learning === | ||
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===== Classes ===== | ===== Classes ===== | ||
*[https://www.coursera.org/course/ml Coursera Machine Learning Course with Andrew Ng] | *[https://www.coursera.org/course/ml Coursera Machine Learning Course with Andrew Ng] | ||
*[https://www.coursera.org/course/compneuro Coursera Computational Neuroscience Course with Adrienne Fairhall] | *[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://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://cs229.stanford.edu/materials.html Stanford CS229] | ||
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*[http://ocw.mit.edu/courses/mathematics/18-06-linear-algebra-spring-2010/ Linear Algebra with Gilbert Strang] | *[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://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 ==== | ==== Books ==== | ||
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*[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://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] | *[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 ==== | ==== Tutorials ==== | ||
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*[http://scikit-learn.org/stable/tutorial/basic/tutorial.html Introduction to ML with scikits.learn] | *[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] | *[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 ==== | ==== Noisebridge ML Class Slides ==== | ||
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** Collective Intelligence & Recommendation Engines | ** Collective Intelligence & Recommendation Engines | ||
[[Category:Events]] | [[Category:Events]] | ||
[[Category:Projects]] | [[Category:Projects]] |
Latest revision as of 18:03, 29 November 2023
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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