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