missing.tex

\chapter{Missing Parts}

\begin{enumerate}
    \item Bayesion Decision Theory: Example of Conditional Probability
    \item Decision Tree: Illustrative Example, nodes splitting strategies
    \item Generalization and performance evaluation: example of overfitting and underfitting 
    \item Linear perceptron: the full algorithm for perceptron learning
    \item Multi-layer perceptron: non-linearly separable example, general structure of multi-layer ANN, backpropagation examples, design issues for ANN
    \item SVM with kernels: lagrange multiplier method, langrangian for primal form, mercer function definition, etc.
    \item Regression: Kernelized Regression
    \item Ensemble Learning
    \item Cluster Analysis: Cohesion and separation using proximity graph based approach, Silhouette Coefficient, Visual Object Classes Challenge
    \item Dimension Reduction: Subset selection, PCA maximize var(z) first and second principal, Matrix factorization, Multidimensional Scaling, Isomap
    \item Non-parametric Density Estimation: advantages and disadvantages of histogram, choosing bandwidth using 3 methods, Product Kernels, full formulas for $c_d$ and gamma function in kNN
    \item Graphical method: Computational complexity of Bayesian Network, Constructing Bayesian Network, Inference in Bayesian Network, Generative Models, Linear Regression, Belief Propagation and Chain, Markov Random Fields
\end{enumerate}