Applied Probability in R | | | |
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Introduction to Machine Learning | | | |
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Generalized Linear Models | | | |
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Support Vector Machines | | | |
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Random Forests | | | |
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Principle Components Analysis (PCA) | | | |
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Graphical Models | | | |
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Clustering | | | |
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Bayesian Inference | | | |
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Mixture Models and Expectation Maximization | | | |
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How to Data-mine Text | | | |
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How to Analyze a Time Series | | | |
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Gaussian Processes | | | |
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Hidden-Markov Models | | | |
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Neural Nets, Deep Belief Nets | | | |
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Computer Vision with OpenCV | | | |
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Reinforcement Learning | | | |
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