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