Association or relationship I remember being taught that correlation is a test of association and regression is test of relationship.
In addition, I remember learning that association means that no assumption is made on which variable is independent and which variable is dependent, whereas relationship implies such a distinction.
My question: Are the terms "association" and "relationship" interchangeable? 
More specifically, if I interpret my findings from a bivariate linear correlation analysis, would it be appropriate to use words such as "there was a strong positive relationship between A and B"?
 A: Under the definitions you've listed, "association" and "relationship" would not be interchangeable.  However, I would argue that a better use of the term "relationship" would make them fairly synonymous in this application.  I think that your teacher was making an important, and correct, point about correlation and regression, but that the way it was done (at least according to your memory) used the term "relationship" in a non-standard way.  I think you are on solid footing to make the claim as you do in your last paragraph.  For more info on the asymmetrical vs. symmetrical nature of regression and correlation, see here.
A: Yes.  You are basically correct.  Regression is used when you want to show how a dependent variable $Y$ is related to one or more independent variables.  When we refer to correlation we are taking about an association.  Regression is often used to predict future responses for $y$ based on given values for $x$.  In least squares regression the predictor variables are assumed to be observed without error and $Y$ has an independent random error term.  There is also error in variables regression where both $X$ and $Y$ are assumed to be observed with error.  For that problem least squares is not the appropriate was to estimate the regression function.  The function $f(x) =E(Y\vert X=x)$ for the model is called the regression function.  Nevertheless the two ideas are intertwined.  The Pearson product moment correlation measures the strength of the linear relationship between $X$ and $Y$.  If you are using a simple linear regression model $Y=bX+a+\epsilon$ where $\epsilon$ is the independent error term in $Y$ and $a$ and $b$ are the intercept and slope parameters respectively then there is a direct relationship between the parameter $b$ and the Pearson correlation coefficient.
