Clarifying some notation in Bishop book In Bishop book, page 4, section 1.1, there's a notation I don't seem to understand what's meant by it. The whole paragraph, with which the section begins, is:


  We begin by introducing a simple regression problem, which we shall use as a running
  example throughout this chapter to motivate a number of key concepts. Suppose
  we observe a real-valued input variable x and we wish to use this observation to
  predict the value of a real-valued target variable t. For the present purposes, it is instructive
  to consider an artificial example using synthetically generated data because
  we then know the precise process that generated the data for comparison against any
  learned model. The data for this example is generated from the function sin(2πx)
  with random noise included in the target values, as described in detail in Appendix A.
  Now suppose that we are given a training set comprising N observations of x,
  written $x \equiv (x_1, . . . , x_N)^T$, together with corresponding observations of the values
  of t, denoted $t \equiv (t_1, . . . , t_N)^T$. Figure 1.2 shows a plot of a training set comprising
  N = 10 data points. The input data set x in Figure 1.2 was generated by choosing
  values of xn, for n = 1, . . . , N, spaced uniformly in range [0, 1], and the target
  data set t was obtained by first computing the corresponding values of the function
  1.1. Example: Polynomial Curve Fitting 5
  sin(2πx) and then adding a small level of random noise having a Gaussian distribution
  (the Gaussian distribution is discussed in Section 1.2.4) to each such point in
  order to obtain the corresponding value tn. By generating data in this way, we are
  capturing a property of many real data sets, namely that they possess an underlying
  regularity, which we wish to learn, but that individual observations are corrupted by
  random noise. This noise might arise from intrinsically stochastic (i.e. random) processes
  such as radioactive decay but more typically is due to there being sources of
  variability that are themselves unobserved.


I'm not clear what the notation  $x \equiv (x_1, . . . , x_N)^T$ might mean here, especially the so called variable T. 
I guess it means it's the set of input vectors together with its corresponding set of target vectors (t). If I am right, so is this an example of  supervised learning? Still, what's T?
$x$ is a vector, composed of the individual elements $x_1$, $x_2$, etc.  The "T" is for transpose.  It is not a variable.