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Prüfer: Prof. Nöth

**Performance Evaluation**

- „Explain the ROC curve“. (TP-rate, FP-rate, drawing, meaning, Area Under Curve)
- „What do we need to be able to use a ROC curve?“ (we need a one-class-yes-or-no-problem, and not a two-or-more-class Problem). That one was very vague and it took me some time until I said what he wanted to hear.

**Bayes Classifier**

- „Explain the Bayes Classifier“. (prior, posterior, Bayes formula, argmax p(y|x))
- „How do we get p(x|y) and p(y)?“ (By doing assumptions about the kind of distribution. Then we can estimate the parameters from the training data.)
- [I took the gaussian distribution as an example for such an assumption, which led over to…]

**Gaussian classifier**

- How to estimate the parameters
- How does the decision boundary look like? (quadratic or sometimes (when?) linear)
- „What can we do in order to get rid of the exp(-1/2 * x^T \Sigma x …) part?“ (What are logistic functions, how to formulate a twoclass problem in terms of logistic functions, role of the F(x), decision boundary is F(x)=0)
- I also explained how the F(x) will look like in case of Gaussian, and how this explains a linear decision boundary in case of equal variances. Not sure whether he wanted to hear that.
- „How large is the covariance matrix of a 100-dimensional-vectorial-data?“ (about ~10000/2 entries (symmetry!), O(n^2))
- „Naive Bayes…“ (…assumes independency of the entries, cov-matrix is diagonal, 100 entries)
- „Something in between?“ (cov-matrix with only diagonal and some minor diagonals)
- „When is this appropriate; why should only some, but not all components be related to each other?“ (time-sampled data or similar)

**Unrelated**

- „What can we do if the dimension is too high?“ (e.g. PCA. no further questions)

**NN- and kNN-Classifier**

- „what does the NN do?“
- „what requirements for the data?“ (must be normalized, all entries should span the same range)
- „how does kNN work?“
- „explain the code“ (detailed explanation of the weird matlab syntax needed!)

**EM-Algorithm**

- „But if we don't have a gaussian, what can we try?“ (maybe GMM/EM-algorithm)
- „Explain the formula“
- „Explain the steps“
- local maximum.

**Summary**

Noeth often expected me to continue his own sentences. Questions were extremely vague.

I would definitely *not* call the atmosphere „kind“. Noeth was pretty picky about minor mistakes, the tone was rather condescending. Nevertheless, the grading was pretty student-friendly and forgiving.