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=== Next Meeting=== | === Next Meeting=== | ||
*When: Wednesday, 3/ | *When: Wednesday, 3/9/2010 @ 7:30-9:00pm | ||
*Where: 2169 Mission St. (back corner classroom) | *Where: 2169 Mission St. (back corner classroom) | ||
*Topic: | *Topic: Analysis of Rock Climbing Data | ||
*Details: | *Details: | ||
*Presenter: | *Presenter: Joe H | ||
=== Future Talks and Topics === | === Future Talks and Topics === |
Revision as of 15:20, 7 March 2011
Next Meeting
- When: Wednesday, 3/9/2010 @ 7:30-9:00pm
- Where: 2169 Mission St. (back corner classroom)
- Topic: Analysis of Rock Climbing Data
- Details:
- Presenter: Joe H
Future Talks and Topics
- Joe H's Climbing Data Analysis (3/9/2011)
- Intro to Machine Learning (3/16/2011)
- Graphical Models, Tony
- Boltzmann Machines (Mike S, March 2011)
- Boosting and Bagging (Thomas, unscheduled)
- CS229 second problem set
- RPy?
Mailing List
https://www.noisebridge.net/mailman/listinfo/ml
Projects
- Small Group Subproblems
- Fundraising
- Noisebridge Machine Learning Course
- Kaggle Social Network Contest
- KDD Competition 2010
- HIV
Datasets
Software Tools
- OpenCV
- Computer Vision Library
- Has ML component (SVM, trees, etc)
- Online tutorials here
- Mahout
- Hadoop cluster based ML package.
- Weka
- a collection of data mining tools and machine learning algorithms.
- MOA (Massive Online Analysis)
- Offshoot of weka, has all online-algorithms
- scikits.learn
- Machine learning Python package
- LIBSVM
- c-based SVM package
- PyML
- MDP
- Orange
- Strong data visualization component
- Journal of Machine Learning Software List
- VirtualBox Virtual Box Image with Pre-installed Libraries listed here
- Theano: Symbolic Expressions and Transparent GPU Integration
Presentations and other Materials
- 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
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 [1]
- 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 [2]
- Information Theory: Entropy, Mutual Information, Gaussian Channels
- Estimation of Misclassification [3]
- No-Free Lunch Theorem [4]
- Machine Learning SDK's
- Applications
- Collective Intelligence & Recommendation Engines