What is the purpose of characteristic functions? I'm hoping that someone can explain, in layman's terms, what a characteristic function is and how it is used in practice. I've read that it is the Fourier transform of the pdf, so I guess I know what it is, but I still don't understand its purpose. If someone could provide an intuitive description of its purpose and perhaps an example of how it is typically used, that would be fantastic!
Just one last note: I have seen the Wikipedia page, but am apparently too dense to understand what is going on. What I'm looking for is an explanation that someone not immersed in the wonders of probability theory, say a computer scientist, could understand.
 A: @charles.y.zheng and @cardinal gave very good answers, I will add my two cents. Yes the characteristic function might look like unnecessary complication, but it is a powerful tool which can get you results. If you are trying to prove something with cumulative distribution function it is always advisable to check whether it is not possible to get the result with characteristic function. This sometimes gives very short proofs. 
Although at first the characteristic function looks unintuitive way of working with probability distributions, there are some powerful results directly related with it, which imply that you cannot discard this concept as a mere mathematical amusement. For example my favorite result in probability theory is that any infinitely divisible distribution has the unique Lévy–Khintchine representation. Combined with the fact that the infinitely divisible distributions are the only possible distribution for limits of sums of independent random variables (excluding bizarre cases) this is a deep result using which central limit theorem is derived. 
A: Back in the day, people used logarithm tables to multiply numbers faster.  Why is this?  Logarithms convert multiplication to addition, since $\log(ab) = \log(a) + \log(b)$.  So in order to multiply two large numbers $a$ and $b$, you found their logarithms, added the logarithms, $z = \log(a) + \log(b)$, and then looked up $\exp(z)$ on another table.
Now, characteristic functions do a similar thing for probability distributions.  Suppose $X$ has a distribution $f$ and $Y$ has a distribution $g$, and $X$ and $Y$ are independent.  Then the distribution of $X+Y$ is the convolution of $f$ and $g$, $f * g$.
Now the characteristic function is an analogy of the "logarithm table trick" for convolution, since if $\phi_f$ is the characteristic function of $f$, then the following relation holds:
$$
\phi_f \phi_g = \phi_{f * g}
$$
Furthermore, also like in the case of logarithms,it is easy to find the inverse of the characteristic function: given $\phi_h$ where $h$ is an unknown density, we can obtain $h$ by the inverse Fourier transform of $\phi_h$.
The characteristic function converts convolution to  multiplication for density functions the same way that logarithms convert multiplication into addition for numbers. Both transformations convert a relatively complicated operation into a relatively simple one.
A: The purpose of characteristic functions is that they can be used to derive  the properties of distributions in probability theory. If you're not interested in such derivations you do not need to learn about characteristic functions.
A: The characteristic function is the Fourier transform of the density function of the distribution. If you have any intuition regarding Fourier transforms, this fact may be enlightening.  The common story about Fourier transforms is that they describe the function 'in frequency space.' Since a probability density is usually unimodal (at least in the real world, or in the models made about the real world), this doesn't seem terribly interesting.
A: The Fourier transformation is a decomposition of the function (non-periodic) in its frequencies. Interpretation for densities?
Fourier transformation is the continuous version of a Fourier series since no density is periodic no expression like "characteristic series".
