Pattern Recognition and Statistical Learning
An introduction to pattern recognition and statistical classifiers.
- nearest neighbor classification and nearest neighbor algorithms
- feature extraction and common feature types
- neural networks, gradient descent
- RBF networks and interpolation
- perceptrons and support vector machines
- k-means clustering, Gaussian mixtures, and semi-supervised learning
- VQ, principal components analysis and compression
- hierarchical clustering, dimensionality reduction
- decision trees
- pattern recognition with graphs
- generative data models and model-based classification
- Bayesian decision theory
- ML and Bayesian parameter estimation