Statistical Data Mining Tutorials
Tutorial Slides by
Andrew Moore
Decision Trees
Information Gain
Probability for Data Miners
Probability Density Functions
Gaussians
Maximum Likelihood Estimation
Gaussian Bayes Classifiers
Cross-Validation
Neural Networks
Instance-based learning (aka Case-based or Memory-based or non-parametric)
Eight Regression Algorithms
Predicting Real-valued Outputs: An introduction to regression
Bayesian Networks
Inference in Bayesian Networks (by Scott Davies and Andrew Moore)
Learning Bayesian Networks
A Short Intro to Naive Bayesian Classifiers
Short Overview of Bayes Nets
Gaussian Mixture Models
K-means and Hierarchical Clustering
Hidden Markov Models
VC dimension
Support Vector Machines
PAC Learning
Markov Decision Processes
Reinforcement Learning
Biosurveillance: An example
Elementary probability and Naive Bayes classifiers
Spatial Surveillance
Time Series Methods
Game Tree Search Algorithms, including Alpha-Beta Search
Zero-Sum Game Theory
Non-zero-sum Game Theory
Introductory overview of time-series-based anomaly detection algorithms
AI Class introduction
Search Algorithms
A-star Heuristic Search
Constraint Satisfaction Algorithms, with applications in Computer Vision and Scheduling
Robot Motion Planning
HillClimbing, Simulated Annealing and Genetic Algorithms
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