Statistical machine learning

1. Introduction
   • Ville
2. Distributions
   • Ville
3. Linear regression
   • Ville
4. Linear models for classification
   • Ville
5. Gaussian processes for regression
   • Isambi
6. Gaussian processes for classification
   • Zubeda
7. Relevance vector machines
   • Jukka
8. Bayes networks
   • Natalia
9. BN Inference
   • Janne
10. BN Approximate inference
    • Idrissa
11. Continuous latent variables
    • Adam

Each student leads the discussion on a single topic. Be prepared to give a 60 minute lecture/seminar presentation and spend 30 minutes for discussion and questions.

Give your slides to Ville before the lecture so that they can be made available to others.

Prepare also 1-3 exercise tasks for other students as well as model solutions to them. These tasks should give some hands on experience of the machine learning approach in question.

If you need any material, hints, or help in preparing the presentation or the exercise tasks, please contact Ville as early as possible.