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tutorials:list.html
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
tutorials/list.html.txt · Last modified: 2016/10/04 21:27 (external edit)
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