% We will use these notes to scribe the lectures in ECE6980. 

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\bf\large ECE 6980 \\
An Algorithmic and Information-Theoretic Toolbox for Massive Data
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\noindent
Instructor: Jayadev Acharya
\hfill Lecture \#0\\
Scribe: \hfill 
23rd August, 2016} 
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\section{Introduction}

We will use this template for scribing. Some examples to fill in:

\begin{definition}
A discrete distribution $P$ over $\cX$ is a function from a countable set $\cX$ to $\RR_+$, such that $\sum_{x\in\cX} P(x) =1$. 
\end{definition}

\begin{theorem}
Let $X$ be a Bernoulli random variable, with $\Probof{X=0}=1-p$. Then
\begin{align}
\EE[X]=p.
\end{align}
\end{theorem}

\begin{proof}
The expected value of a discrete random variable is:
\begin{align}
\EE[X] &= \sum_{x\in\cX} x\cdot P(x)\\
&= 0\cdot (1-p) + 1\cdot p = p.
\end{align}
\end{proof}

Please search on the internet/email me if you are stuck with anything while
writing these. 



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