add prml/01_introduction.md

clrs/14_augmenting_data_structures.md

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+## Exercises
+### 14.1-3

prml/01_introduction.md

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+## Exercises
+### 1.1
+\begin{align*}
+&\sum_{j=0}^M A_{ij}w_j &=& T_i \\
+&\sum_{j=0}^M \sum_{n=1}^N (x_n)^{i+j} w_j &=& \sum_{n=1}^N(x_n)^i t_n \\
+&\sum_{n=1}^N \sum_{j=0}^M (x_n)^{i+j} w_j - \sum_{n=1}^N(x_n)^i t_n &=& 0 \\
+&\sum_{n=1}^N (\sum_{j=0}^M (x_n)^{i+j} w_j - (x_n)^i t_n) &=& 0 \\
+&\sum_{n=1}^N ( (x_n)^i \sum_{j=0}^M (x_n)^j w_j - (x_n)^i t_n) &=& 0 \\
+&\sum_{n=1}^N (x_n)^i (\sum_{j=0}^M (x_n)^j w_j - t_n) &=& 0 \\
+&\sum_{n=1}^N (x_n)^i (y(x_n, \mathbf{w}) - t_n) &=& 0 \\
+\end{align*}
+
+Take derivative of
+$$E(\mathbf{w}) = \frac{1}{2}\sum_{n=1}^N {y(x_n, \mathbf{w}) - t_n}^2$$
+in terms of $\mathbf{x}$, then we get
+\begin{align*}
+\frac{\partial E}{\partial \mathbf{x}} &=& \sum_{n=1}^N (y(x_n, \mathbf{w}) - t_n) \\
+&=&
+\end{align*}

prml/README.md

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+### Compile to pdf
+
+    pandoc _input.md_ -o _output_.pdf
+
+It would seem more reasonable to choose the complexity of the model according to the complexity of the problem being solved.