Examiner: Prof. Nöth
Atmosphere: Friendly. He helped me out when I was stuck and didn't get
where he wanted to go with his question. He seemed to do the whole exam
spontaneously and included some short lecturelike sequences. He graded
me 1.0 even though I was stuck some time and gave 1-2 incorrect answers.
Bayes-Classifier
decision rule
pdf modelling (Gaussian etc.)
drawing a decision boundary (offset through priors etc.)
relationship to KNN (P(bayesian) ⇐ P(nearest_neighbour)⇐ 2*p(bayesian))
naive Bayes (impact on decision boundary)
same covariance matrices (linear decision boundary)
LDA
steps (joint covariance matrix, SVD, calculation of the transform,
apply transform to class means)
decision rule
GMM
equation (summation of the components)
EM algorithm (write down p_ik, µ, etc.)
initialization of the EM algorithm (random, k-means, apply repeatedly)
drawbacks (local algorithm, slow convergence)
Boosting