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=== 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 [[Category:Events]] [[Category:Projects]]
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