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\begin{document}
\title{\bf Sample questions for the Pattern Recognition Course test }
\author{\bf J. Matas }
\date{\today}
\maketitle

\begin{enumerate}
\item In Bayesian Decision Making, define the risk (i.e. expected loss)
      of a strategy (i.e. of a decision rule).

\item Minimum-error-rate classification and its relation to Bayesian risk
      minimization.

\item Describe limitations of Bayesian decision making. 

\item What is the decision function for a two-class classification problem
       The distribution of observations in both classes is Normal, with
       identical covariance matrices C; their means differ.

\item Define a classification problem with the the reject option.
\item  Define the Neyman-Pearson formulation of the decision.
       What is the optimal decision strategy for the problem?
\item  Define the min-max formulation of the classification problem.
       What is the optimal decision strategy for the problem?
\item  Define Wald decision making problem.
       What is the optimal decision strategy for the problem?

\item Describe the Maximum Likelihood method for parameter estimation. 

\item Density estimation via Parzen windows.
\item Describe the k nearest-neighbour rule.

\item Specify at least 5 properties of the nearest-neighbour classifier.
\item Discuss speed-up techniques  for the nearest-neighbour rule.

\item Describe the perceptron learning algorithm and its properties.
      Explain how the perceptron learning algorithm can be viewed
      as gradient descent.

\item Linear decision function: describe one or more statistical models,
     i.e. conditional probabilities $P(x|k)$, where linear decision
     function is optimal.

\item Describe a feed-forward Neural Net.
 
\item The back propagation algorithm and its properties.

\item Describe a Support Vector Machine. Define the optimization task
      solved in SVM learning.

\item Compare learning of feed-forward neural net by back-propagation and
      SVM learning.

\item Which properties a kernel function possess? 
      Examples of kernels commonly used in SVM learning.

\item What are the differences in properties of the SVM and perceptron
       learning algorithms?

\item Define the empirical risk (of classifier on a training set T) 
      and its structural risk.

\item How is non-separability of data handled in SVM learning?

\item The Adaboost learning algorithm. Description, properties.

\item The EM algorithm.


\end{enumerate}
\end{document}



