# Machine Learning

## Contents

### Next Meeting

- When: Wednesday, 5/4/2011 @ 7:30-9:00pm
- Where: 2169 Mission St. (back corner classroom)
- Topic: Collaborative Q&A
- Details: Come in with your ML questions and we'll try to answer them as a group.
- Presenter:

### Future Talks and Topics

- Graphical Models, Tony
- Boltzmann Machines (Mike S, May 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

- UCI Machine Learning Repository
- DataSF.org
- Infochimps
- Face Recognition Databases
- Time Series Data Library

### 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
- Does not stand for Markov Decision Process :(

- 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
- Waffles
- Open source C++ set of machine learning command line tools.

- RapidMiner
- Mobile Robotic Programming Toolkit

### 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