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## Pattern Recognition

- The first question was about the performance evaluation of classifiers. Definition of recognition rate, recall and precision.
- The Bayesian classifier and I had to write the decision rule using Bayes formula. What each of the terms meant and how can we calculate them (estiation of parameters). Also I mentioned optimality with respect to the 0/1 loss function, so he asked what are different loss functions that we can use and when it is appropriate to use it.
- At if we have too many parameters to estimate? What can we do? (Use naïve bayes or to use feature transform)
- Than in connection to the feature transform he asked me to explain PCA.
- What is a perceptron and how can we get the decision boundary of it?
- How we can use perceptron in case of AdaBoosting.
- He then asked me about the Viola Jones algorithm and how boosting is used in it?
- What are SVMs? I explained both soft and hard decision case.
- If we have any distribution how can we model it using gaussians? What are the different parameters we have to estimate and how are they estimated? He drew a diagram of a distribution with initial means assigned and asked me what will happen after the first step?
- The Gaussian mixture model, what happens in the expectation and maximization step and which parameters are estimated in each case.
- He then draw down 3 probabilities for the 3 gaussians and asked me how would i perform EM algorith for given measurement points.

More or less that was what he asked me. Usually, the next question was connected to the previous one.