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)  Nonparametric density estimation. Histograms, kernel density estimation and nearestneighbour 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. Biasvariance decomposition. 
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)  Feedforward network functions. Network training, parameter optimisation, local quadratic approximation, gradient information and gradient descent optimisation. Error backpropagation. 
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 NadarayaWatson 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! 