Indexing of Discrete Fourier Transforms I was looking at the Discrete Fourier Tranform section in these notes  and I'm very confused about how the transform is being indexed.
There, a list of the form $f(x_n)$ is given where $x_n$ takes on the values $x_n\in \{0,1,\ldots,N\}$, that is $x_n=n$.  It is then claimed that $f(x_n)$ is the Discrete Fourier Transform of a list $f(k_j)$ if the two are related by
\begin{align}
f(x_n)=\sum_{j=0}^N f(k_j)\exp (-ix_n k_j)
\end{align}
where $k_j$ takes on the values $k_{j}\in \frac{2\pi}{N}\{0,1,\ldots, \frac{N}{2},1-\frac{N}{2}, 2-\frac{N}{2}, \ldots , -1\}$, that is
\begin{align}
     k_{j}&=\begin{cases} \frac{2\pi}{N}j & 0\le j\le N/2\\
     \frac{2\pi}{N}\left (j-N\right ) & N/2 \le j\le N-1\ .
     \end{cases}
     \end{align}
What is going on with this indexing?  Why don't we just have $k_j=\frac{2\pi}{N}j$?
This kind of indexing isn't what the Wikipedia page on Fourier transforming has, but it seems commonly used in code involving Fast Fourier Transforms, see the code on this page, for example.

Adding or subtracting a multiple of $2\pi$ from $k_j$ does not affect the values of the sum $\sum_{j=0}^N f(k_j)\exp (-ix_n k_j)$ at the points $x_n$. Thus, either form could be used if all we ever going to plug for $x_n$ are integers. (Or we could randomly add $10\pi$ to all the $k_j$.)
Aliasing is the term used for this agreement of different trigonometric waves on a discrete grid. 
But if at any point someone will want to recover the sampled signal from the Fourier coefficients, then non-integer values of $x$ may come into play, and then the $2\pi$ frequency shift matters. Lower frequencies are preferred*, which means that, among all $k_j+2\pi \mathbb{Z}$ possibilities we should use the one with the smallest absolute value. This leads to the indexing you noticed. 
Why should we prefer lower frequencies? Because out of the infinitely many trigonometric polynomials passing through given points, the one with the lowest frequencies possible minimizes the energy $\int |f'|^2$. Indeed, by virtue of orthogonality (Parseval's theorem) each term $c\exp (i\omega t)$ contributes some multiple of $|c|^2|\omega|^2$ to the energy, hence we minimize the energy by associating the Fourier coefficient $c$  with the lowest possible value of $|\omega|$.  Minimizing energy eliminates extraneous oscillation in the recovered signal (see the graph below), and is a standard approach in signal/image restoration. Incidentally, if we were not restricted to using trigonometric polynomials, minimization of $\int |f'|^2$ among all functions passing through given points would produce a natural cubic spline (my blog post Connecting dots naturally). 
I have a small illustration handy (for the discrete cosine transform, but the principle is the same): blue dots are the sampled values, red curve is constructed by using Fourier coefficients in the manner of $k_j = 2\pi j/N$, while in the green curve the high-frequency terms are replaced by their lower-frequency aliases. Clearly, the green curve is the one we want here. 

Source: my blog post Poor man’s discrete cosine transform.
If the above is not convincing, here is another reason: trigonometric sums
$$
\sum_{k=0}^n (A_k \cos kx + B_k\sin kx)
$$
naturally correspond to exponential sums
$$
\sum_{k=-n}^n C_k \exp(i kx)
$$
not to 
$$
\sum_{k=0}^{2n} C_k \exp(i kx)
$$
