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+\section{Solving Equations}
+
+\begin{frame}[fragile]
+\frametitle{Solution of linear equations}
+Consider,
+  \begin{align*}
+    3x + 2y - z  & = 1 \\
+    2x - 2y + 4z  & = -2 \\
+    -x + \frac{1}{2}y -z & = 0
+  \end{align*}
+Solution:
+  \begin{align*}
+    x & = 1 \\
+    y & = -2 \\
+    z & = -2
+  \end{align*}
+\end{frame}
+
+\begin{frame}[fragile]
+\frametitle{Solving using Matrices}
+Let us now look at how to solve this using \texttt{matrices}
+  \begin{lstlisting}
+In []: A = array([[3,2,-1],
+                  [2,-2,4],                   
+                  [-1, 0.5, -1]])
+In []: b = array([1, -2, 0])
+In []: x = solve(A, b)
+  \end{lstlisting}
+\end{frame}
+
+\begin{frame}[fragile]
+\frametitle{Solution:}
+\begin{lstlisting}
+In []: x
+Out[]: array([ 1., -2., -2.])
+\end{lstlisting}
+\end{frame}
+
+\begin{frame}[fragile]
+\frametitle{Let's check!}
+\begin{small}
+\begin{lstlisting}
+In []: Ax = dot(A, x)
+In []: Ax
+Out[]: array([ 1.00000000e+00,  -2.00000000e+00, 
+              -1.11022302e-16])
+\end{lstlisting}
+\end{small}
+\begin{block}{}
+The last term in the matrix is actually \alert{0}!\\
+We can use \texttt{allclose()} to check.
+\end{block}
+\begin{lstlisting}
+In []: allclose(Ax, b)
+Out[]: True
+\end{lstlisting}
+\end{frame}
+
+\begin{frame}[fragile]
+\frametitle{\texttt{roots} of polynomials}
+\begin{itemize}
+\item \texttt{roots} function can find roots of polynomials
+\item To calculate the roots of $x^2-5x+6$ 
+\end{itemize}
+\begin{lstlisting}
+  In []: coeffs = [1, -5, 6]
+  In []: roots(coeffs)
+  Out[]: array([3., 2.])
+\end{lstlisting}
+\vspace*{-.2in}
+\begin{center}
+\includegraphics[height=1.6in, interpolate=true]{images/roots}    
+\end{center}
+\end{frame}
+
+\begin{frame}[fragile]
+\frametitle{SciPy: \texttt{fsolve}}
+\begin{small}
+\begin{lstlisting}
+  In []: from scipy.optimize import fsolve
+\end{lstlisting}
+\end{small}
+\begin{itemize}
+\item Finds the roots of a system of non-linear equations
+\item Input arguments - Function and initial estimate
+\item Returns the solution
+\end{itemize}
+\end{frame}
+
+\begin{frame}[fragile]
+\frametitle{\texttt{fsolve} \ldots}
+Find the root of $sin(z)+cos^2(z)$ nearest to $0$
+\begin{lstlisting}
+In []: def g(z):
+ ....:     return sin(z)+cos(z)*cos(z)
+
+In []: fsolve(g, 0)
+Out[]: -0.66623943249251527
+\end{lstlisting}
+\begin{center}
+\includegraphics[height=2in, interpolate=true]{images/fsolve}    
+\end{center}
+\end{frame}
+
+\section{ODEs}
+
+\begin{frame}[fragile]
+\frametitle{Solving ODEs using SciPy}
+\begin{itemize}
+\item Consider the spread of an epidemic in a population
+\item $\frac{dy}{dt} = ky(L-y)$ gives the spread of the disease
+\item $L$ is the total population.
+\item Use $L = 2.5E5, k = 3E-5, y(0) = 250$
+\item Define a function as below
+\end{itemize}
+\begin{lstlisting}
+In []: from scipy.integrate import odeint
+In []: def epid(y, t):
+  ....     k = 3.0e-5
+  ....     L = 2.5e5
+  ....     return k*y*(L-y)
+  ....
+\end{lstlisting}
+\end{frame}
+
+\begin{frame}[fragile]
+\frametitle{Solving ODEs using SciPy \ldots}
+\begin{lstlisting}
+In []: t = linspace(0, 12, 61)
+
+In []: y = odeint(epid, 250, t)
+
+In []: plot(t, y)
+\end{lstlisting}
+%Insert Plot
+\end{frame}
+
+\begin{frame}[fragile]
+\frametitle{Result}
+\begin{center}
+\includegraphics[height=2in, interpolate=true]{images/epid}  
+\end{center}
+\end{frame}
+
+
+\begin{frame}[fragile]
+\frametitle{ODEs - Simple Pendulum}
+We shall use the simple ODE of a simple pendulum. 
+\begin{equation*}
+\ddot{\theta} = -\frac{g}{L}sin(\theta)
+\end{equation*}
+\begin{itemize}
+\item This equation can be written as a system of two first order ODEs
+\end{itemize}
+\begin{align}
+\dot{\theta} &= \omega \\
+\dot{\omega} &= -\frac{g}{L}sin(\theta) \\
+ \text{At}\ t &= 0 : \nonumber \\
+ \theta = \theta_0(10^o)\quad & \&\quad  \omega = 0\ (Initial\ values)\nonumber 
+\end{align}
+\end{frame}
+
+\begin{frame}[fragile]
+\frametitle{ODEs - Simple Pendulum \ldots}
+\begin{itemize}
+\item Use \texttt{odeint} to do the integration
+\end{itemize}
+\begin{lstlisting}
+In []: def pend_int(initial, t):
+  ....     theta = initial[0]
+  ....     omega = initial[1]
+  ....     g = 9.81
+  ....     L = 0.2
+  ....     F=[omega, -(g/L)*sin(theta)]
+  ....     return F
+  ....
+\end{lstlisting}
+\end{frame}
+
+\begin{frame}[fragile]
+\frametitle{ODEs - Simple Pendulum \ldots}
+\begin{itemize}
+\item \texttt{t} is the time variable \\ 
+\item \texttt{initial} has the initial values
+\end{itemize}
+\begin{lstlisting}
+In []: t = linspace(0, 20, 101)
+In []: initial = [10*2*pi/360, 0]
+\end{lstlisting} 
+\end{frame}
+
+\begin{frame}[fragile]
+\frametitle{ODEs - Simple Pendulum \ldots}
+%%\begin{small}
+\texttt{}
+%%\end{small}
+\begin{lstlisting}
+In []: from scipy.integrate import odeint
+In []: pend_sol = odeint(pend_int, 
+                         initial,t)
+\end{lstlisting}
+\end{frame}
+
+\begin{frame}[fragile]
+\frametitle{Result}
+\begin{center}
+\includegraphics[height=2in, interpolate=true]{images/ode}  
+\end{center}
+\end{frame}
+
+%% \section{FFTs}
+
+%% \begin{frame}[fragile]
+%% \frametitle{The FFT}
+%% \begin{itemize}
+%%     \item We have a simple signal $y(t)$
+%%     \item Find the FFT and plot it
+%% \end{itemize}
+%% \begin{lstlisting}
+%% In []: t = linspace(0, 2*pi, 500)
+%% In []: y = sin(4*pi*t)
+
+%% In []: f = fft(y)
+%% In []: freq = fftfreq(500, t[1] - t[0])
+
+%% In []: plot(freq[:250], abs(f)[:250])
+%% In []: grid()
+%% \end{lstlisting} 
+%% \end{frame}
+
+%% \begin{frame}[fragile]
+%% \frametitle{FFTs cont\dots}
+%% \begin{lstlisting}
+%% In []: y1 = ifft(f) # inverse FFT
+%% In []: allclose(y, y1)
+%% Out[]: True
+%% \end{lstlisting} 
+%% \end{frame}
+
+%% \begin{frame}[fragile]
+%% \frametitle{FFTs cont\dots}
+%% Let us add some noise to the signal
+%% \begin{lstlisting}
+%% In []: yr = y + random(size=500)*0.2
+%% In []: yn = y + normal(size=500)*0.2
+
+%% In []: plot(t, yr)
+%% In []: figure()
+%% In []: plot(freq[:250],
+%%   ...:      abs(fft(yn))[:250])
+%% \end{lstlisting}
+%% \begin{itemize}
+%%     \item \texttt{random}: produces uniform deviates in $[0, 1)$
+%%     \item \texttt{normal}: draws random samples from a Gaussian
+%%         distribution
+%%     \item Useful to create a random matrix of any shape
+%% \end{itemize}
+%% \end{frame}
+
+%% \begin{frame}[fragile]
+%% \frametitle{FFTs cont\dots}
+%% Filter the noisy signal:
+%% \begin{lstlisting}
+%% In []: from scipy import signal
+%% In []: yc = signal.wiener(yn, 5)
+%% In []: clf()
+%% In []: plot(t, yc)
+%% In []: figure()
+%% In []: plot(freq[:250], 
+%%   ...:      abs(fft(yc))[:250])
+%% \end{lstlisting}
+%% Only scratched the surface here \dots
+%% \end{frame}