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tutorials:list.html

# Statistical Data Mining Tutorials

# Tutorial Slides by Andrew Moore

* [Decision Trees][25]
* [Information Gain][26]
* [Probability for Data Miners][27]
* [Probability Density Functions][28]
* [Gaussians][29]
* [Maximum Likelihood Estimation][30]
* [Gaussian Bayes Classifiers][31]
* [Cross-Validation][32]
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* [Instance-based learning (aka Case-based or Memory-based or non-parametric)][34]
* [Eight Regression Algorithms][35]
* [Predicting Real-valued Outputs: An introduction to regression][36]
* [Bayesian Networks][37]
* [Inference in Bayesian Networks (by Scott Davies and Andrew Moore)][38]
* [Learning Bayesian Networks][39]
* [A Short Intro to Naive Bayesian Classifiers][40]
* [Short Overview of Bayes Nets][41]
* [Gaussian Mixture Models][42]
* [K-means and Hierarchical Clustering][43]
* [Hidden Markov Models][44]
* [VC dimension][45]
* [Support Vector Machines][46]
* [PAC Learning][47]
* [Markov Decision Processes][48]
* [Reinforcement Learning][49]
* [Biosurveillance: An example][50]
* [Elementary probability and Naive Bayes classifiers][51]
* [Spatial Surveillance][52]
* [Time Series Methods][53]
* [Game Tree Search Algorithms, including Alpha-Beta Search][54]
* [Zero-Sum Game Theory][55]
* [Non-zero-sum Game Theory][56]
* [Introductory overview of time-series-based anomaly detection algorithms][57]
* [AI Class introduction][58]
* [Search Algorithms][59]
* [A-star Heuristic Search][60]
* [Constraint Satisfaction Algorithms, with applications in Computer Vision and Scheduling][61]
* [Robot Motion Planning][62]
* [HillClimbing, Simulated Annealing and Genetic Algorithms][63]

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