Francesco (F) (francesco dot corona at aalto dot fi)
Rafael (R) (rafael dot de dot oliveira dot e dot lima at gmail dot com)
Edmilson (E) (eqfilho at sfiec dot org dot br)
The material in the slides can be complemented using material from the following textbooks (list not exhaustive).
|THU Aug 20||00. Introduction (F)||Course introduction.|
|TUE Aug 25||01. Probability theory (F)||Generalities, densities, expectations and covariances, Bayesian probabilities, the univariate Gaussian.|
|THU Aug 27||02. Decision theory (F)||Generalities, misclassification rates, expected losses, loss for regression.|
|TUE Sep 01||03. Information theory (F)||Generalities, entropy and differential entropy, conditional entropy, relative entropy and mutual information.|
|THU Sep 03||04. Exercises (R and E)||Probability, decision and information theory.|
|TUE Sep 08||05. Probability distributions (F)||The binomial distribution, Bernoulli and beta distributions and the beta prior. |
Multinomial distributions, the generalised Bernoulli distribution and the Dirichlet prior.
|THU Sep 10||06. Probability distributions (F)||The Gaussian distribution, conditional and marginal Gaussians.|
|TUE Sep 15||07. Probability distributions (F)||Bayes' theorem and maximum likelihood for the Gaussian.|
Bayesian inference for the Gaussian.
Mixture of Gaussians.
|THU Sep 17||08. Probability distributions (F)||Non-parametric density estimation.|
Histograms, kernel density estimation and nearest-neighbour methods.
|TUE Sep 22||09. Exercises (R and E)||Probability distributions.|
|THU Sep 24||10. Linear models for regression (F)||Linear basis function models, maximum likelihood and least squares, regularised least squares, and multiple outputs.|
|TUE Sep 29||11. Linear models for regression (F)||Bayesian linear regression, parameter distribution and predictive distribution.|
The equivalent kernel.
|THU Oct 01||12. Exercises (R and E)||Linear models for regression.|
|TUE Oct 06||13. Linear models for classification (F)||Discriminant functions, Fisher's linear discriminant and the perceptron.|
|THU Oct 08||14. Linear models for classification (F)||Probabilistic generative models.|
|TUE Oct 13||15. Linear models for classification (F)||Probabilistic discriminative models, Logistic regression and probit regression.|
|THU Oct 15||16. Exercises (R and E)||Linear models for classification.|
|TUE Oct 20||17. Neural networks (F)||Feed-forward network functions. |
Network training, parameter optimisation, local quadratic approximation, gradient information and gradient descent optimisation.
|THU Oct 22||18. Exercises (R and E)||Recap.|
|TUE Oct 27||19. Kernel methods (F)||Dual representations and constructing kernels. |
Radial basis functions networks and the Nadaraya-Watson model.
|THU Oct 29||20. Kernel methods (F)||Gaussian processes.|
|TUE Nov 03||21. Exercises (R and E)||Kernel methods.|
|THU Nov 05||22. Sparse kernel methods (F)||Maximum margin classifiers.|
|TUE Nov 10||23. Sparse kernel methods (F)||Support vector regression.|
|THU Nov 12||24. Exercises (R and E)||Sparse kernel methods.|
|TUE Nov 17||25. Exercises (R and E)||Recap.|
|THU Nov 19||26. Exercises (R and E)||Recap.|
|TUE Nov 24||27. Exercises (R and E)||Recap.|
|THU Nov 26||28. Exercises (R and E)||Recap.|
|TUE Dec 01||29. Exercises (R and E)||Recap.|
|THU Dec 03||30. Exercises (R and E)||Recap.|
|TUE Dec 08||31. Grafical models (F)||Introduction to undirected (Bayes networks), directed (Markov networks) and factor graphs. |
Last lecture ya'll!