Uniform distribution theory and jittered sampling

Distributing $N$ points as evenly as possible on the bounded unit interval $[0,1]$ is fairly easy; the set $\{i/N : 1\leq i \leq N \}$ will do the job. But how to generalise this?
In this talk I will introduce star discrepancy as a concept to quantify irregularities of distribution of finite point sets and infinite sequences of points in the $d$-dimensional unit cube. In a second step I will discuss different constructions of point sets and sequences and their discrepancy estimates. I will discuss deterministic and random constructions as well as a combination of both; i.e. so called jittered sampling.
Finally, I will give a short outlook why such constructions are interesting and important.