Estimation and Probabilistic Learning

Farzad Farnoud, University of Virginia
  1. Review of Probability
  2. Probability in Reasoning, Inference, and Learning
  3. Frequentist Parameter Estimation
  4. Bayesian Parameter Estimation (PDF)
  5. Multivariate Random Variables (PDF)
  6. Linear Regression (PDF)
  7. Linear Classification (PDF)
  8. Expectation-Maximization * (PDF)
  9. Basics of Graphical Models (PDF)
  10. Independence in Graphical Models (PDF)
  11. Parameter Estimation in Graphical Models (PDF)
  12. Inference in Graphical Models (PDF)
  13. Inference in Hidden Markov Models (PDF)
  14. Factor Graphs and Sum/Max-product Algorithms ** (PDF)
  15. Markov Chains (PDF)
  16. Sampling Methods (PDF)
  17. Variational Inference * (PDF)
  18. Appendix (PDF)