Prüfer: Prof. Nöth

**Bayes Classifier**
  * What are posteriors/priors?
  * Why it is optimal, how can we compute all probabilities?
  * How can we estimate these parameters?
  * Why it is not always a good idea to use the 0-1 cost function and therefore the missclassification?

**Gaussian classifier**
  * Relationship to the Bayes classifier?
  * How can it computed?
  * How can we estimate the parameters?

**EM-Algorithm**
  * Why and how does the EM converge?
  * Compute the E- and M-step.

**Exercise**
  * kNN example code. Describe what is going on.
  * What are the sizes of the matrices?

**Summary**

Very kind and relaxed atmosphere. Focus on practical considerations!
Describe the Matlab code step by step and focus on the Matlab syntax!
Student friendly grading.