====== Prüfung Pattern Recognition, SS 2015 (Oktober) ======
=====  Prüfer/Examiner=====
Elmar Noeth
===== Prüfungsfragen und Antworten =====
**1. Bayesian Classifier** \\
- What is Bayesian Classifier and what is the bayesian decision rule ?\\
- Can you write down the Bayesian formula ?\\
- What is p(x/y) ?\\
- Why do we ignore the evidence p(x)?\\
- Then he drew two gausian distributions with unequal co-variance matrix and
asked me , what would be the shape of the decission boundry ?\\
- What happens  to the decision boundary if the Prior P(y) of one class is
higher than the other ?\\
- What other distributions do we have other than Gausian ?\\

**2. Naive Bayes**\\
- How many parameters do we need to estimate for a 50 dimentional feature vector ?\\
- What should i do if I want to reduce the parameters to be estimated ?\\
- What does Naive bayes do ?\\

**3. Support Vector Machines**\\
- What do i do when i have linearly separable cases and I need a unique
decision boundary ?\\
- How does the support vector machine work ?\\
- Can you write the optimization rule in both hard and soft margin case with
constraints ?\\

- What will happen If i use perceptron in the same very case ?\\
- When does the perceptron converge ?\\

**4. Gausian Mixture models**\\
- What are gausian mixture models ?\\
- How do we solve the problem of gausian mixture models ?\\
- Explain how E-M Algorithm works ?\\
- What is the constraint of the P(k/y) ? Sum of P(k/y) = 1 ?\\

**5. Adaboost**
- What is a definition of a weak classifier ?\\
- What happens in Adaboost ?\\
- How are misclasified features treated in the next classifier ?\\
- How is the decission made in the end ?\\
- There is a modification to Adaboost algorithm , that is used for face
detection , what is it called and what does it do ?\\
- Why does subsequent classifier perform better ? (Because it has more features.)\\
===== Stimmung/Atmosphere=====
Overall the atmosphere was very friendly , the professor wants to keeps
the discussion very interactive , He likes when you keep on discussing
more about the subject and the question asked. Mostly evaluations are done
when you use certain key words like a name of a classifier or something .
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Note: 1.3