1) Recall Euler's formulae:
$\sin(kw_0t)=1/2(e^{ikw_0t}-e^{-ikw_0t})$
$\cos(kw_0t)=1/2(e^{ikw_0t}+e^{-ikw_0t})$
If your signal is a linear combination of both $\sin$ and $\cos$ then you've got to include $e^{ikw_0t}$ with both positive and negative $k$.
2) Those integral are convolutions, functional analysis analogues of "inner products" that one encounters in analytic geometry. IMO the best way to look at Fourier series by analogy with finite dimensional analytic geometry as follows.
Suppose you have $n$-dimensional space with an orthonormal basis $v_1,...,v_n$, and you want to express a vector $x$ in that basis. What you do is take inner product of $x$ with each of basis vectors and that gives you the coefficients:
$x=\Sigma_{k=1}^nX_kv_k$
where the $k$-th coefficient is given by inner product with $v_k$:
$X_k=x\cdot v_k$
Now, the linear space of (certain) functions is infinite-dimensional. However, if one restricts to "square-integrable" functions the above formulae still hold, with some important clarifications. First, the series become infinite. Second, one needs to properly define what "inner product" is.
It turns out that one can define inner product between square integrable functions as their convolution, and all the usual properties of inner product would hold.