Machine Learning Contents

  • Introduction to machine learning
  • Spectral methods:
    • Eigenvalues and the SVD.
    • Google
    • Text retrieval
    • Image compression
    • Data visualization and PCA
  • Linear models:
    • Least squares
    • Ridge regression & cross-validation
    • Constrained optimization, lasso and feature selection
    • Maximum likelihood and Bayesian learning
    • Conjugate analysis
    • Introduction to MCMC
    • Kalman & particle filters
  • Fast N-body learning
    • KD trees
    • Dual trees
    • Distance transform
    • Fast multipole methods
  • Kernel methods:
    • Kernel ridge regression
    • Kernel lasso
    • Gaussian processes for prediction
    • Semi-supervised learning with kernels
    • RKHS
    • Support vector machines
    • Kernel PCA
    • Kernels on structured data
    • Learning with Gaussian processes
      • MCMC
      • Variational methods
      • Laplace approximation
    • Latent processes
    • Relevance vector machines
    • Online classification with particle filters
  • Active learning
    • Bayesian and minimax decision theory
    • Active learning with Gaussian processes
  • Exponential families
    • Sufficient statistics
    • Maximum likelihood, KL and maximum entropy
    • Generalized linear models
    • Exponential families on graphs
    • Exponential families and AI