Editing Machine Learning (section)

=== 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


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