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* Prove that risk is linear via. expected value
* Bayes Rules calculation with concrete values $p(a=1 | b=0)
* Advantages Baysian learning
* Lagrange very simple example calculation
* SEE in matrix form
* What is the Advantage of SSE for optimization
* Advantages of different aspects of k-means
* Describe the k-means algorithm
* Difference generative and discriminative
* Properties of kernel functions. (Yes/No, senseless definition questions)
* Kernelized SVM decision function with advantages and disadvantages
* Bias variance tradoff explanation, explicitly without formula