Classification of critical points for two variables -- determinant vs definiteness In first year we were taught to classify stationary points using the determinant of the Hessian matrix -- which was procedural and simple enough. 
In second year we were introduced to classifying them using eigenvalues and the positive-definiteness... of the Hessian matrix.  
I currently see no need of introducing these if it's more complicated just for the same task of classifying stationary points.  
Is this somewhat a more "formal" way of classifying stationary points or am I missing the point here?
 A: I'm assuming that you're discussing the classification of extrema in multivariable calculus, wherein we use the second partial derivative test. The first note is that you didn't use just the determinant of the Hessian matrix $H$! Classifying $\det(H)$ as positive / negative / zero was only one decision point in the algorithm. In effect, that algorithm guides you towards whether $H$ is positive definite / negative definite/ has both positive & negative eigenvalues without needing to discuss these terms (requires linear algebra knowledge from students). 
In effect, you're doing the same thing, but now have the terminology of positive definite matrices. 

Definition: A square symmetric matrix $H$ is positive definite if $ \vec{x}^T H \vec{x} >0$ for any vector $\vec{x}\neq \vec{0}$. 

You're currently using the following theorem:

Theorem: $H$ is positive definite $\iff$ all eigenvalues of $H$ are positive ($\lambda_i >0$ for $\lambda_i$ an eigenvalue). 

Whereas the "Intro to Multivariable Calculus w/o a lot of Linear Algebra" course relies on 

Theorem (Sylvester's criterion): $H$ is positive definite $\iff$ all the upper-left determinants of $H$ are positive (these are also know as the leading principal minors of $H$). Alternatively, we can check all the lower-right determinants of $H$.

Notice that showing a critical point of $f(x,y)$ is a minimum using the second partial derivative test requires $\det(H(x_0, y_0))>0$ and $f_{xx}(x_0,y_0) >0$, which are precisely the upper-left determinants. Furthermore, $H$ is symmetric for all "reasonable" Intro to Multivariable Calculus problems due to Clairaut's Theorem ($f_{xy} = f_{yx}$ for nice functions). 
Saddle points, inconclusive test
Furthermore, consider the other conclusions for the second derivative test on $f(x,y)$:


*

*($\det(H) < 0 \implies$ saddle point). Since $\det(H)$ is the product of the eigenvalues, we necessarily have one positive and one negative eigenvalue and thus a saddle. Note that eigenvalues of a real matrix occur as complex conjugate pairs, thus if a $2 \times 2$ matrix has eigenvalues of $\alpha \pm i \beta$, we would have $\det(H) = (\alpha + i \beta)(\alpha - i \beta) = \alpha^2 + \beta^2 >0$. 

*($\det(H) = 0 \implies$ test fails). If $\det(H) =0$, then $0$ is an eigenvalue of $H$ as the determinant is the product of the eigenvalues. Note that just knowing $0$ is an eigenvalue is insufficient to classify as a min/max/saddle (this is true for the general case as well). We could even still have a postive/negative semi-definite matrix. Regardless, we would need to look at even higher order terms in the Taylor series of $f(x,y)$...speaking of which...


Why is knowing that $H$ is positive definite enough?
For simplicity of notation, suppose that $(0,0)$ is a critical point of $f(x,y)$. There is a Taylor series expansion for $f$ about $(0,0)$, letting all partial derivatives be evaluated at $(0,0)$:
\begin{align*} 
 f(x,y) &= f(0,0) + x f_x + y f_y + \frac{1}{2} \left[ x^2 f_{xx} + xy f_{xy} + yx f_{yx} + y^2 f_{yy} \right] + \cdots \\
 &= f(0,0) + (\nabla f )^T \begin{bmatrix}x \\ y\end{bmatrix} + \frac{1}{2} \begin{bmatrix}x \\ y\end{bmatrix}^T \begin{bmatrix} f_{xx} & f_{xy} \\ f_{yx} & f_{yy} \end{bmatrix} \begin{bmatrix}x \\ y\end{bmatrix} +\cdots 
\end{align*}
However, we have that $\nabla f = \vec{0}$ since we're supposed to be at a critical point. Furthermore, the matrix of second partial derivatives is just the Hession matrix $H$. Letting $\vec{x} = \begin{bmatrix} x \\ y \end{bmatrix}$, we can write 
\begin{align*} f(x,y) = f(\vec{x}) = f(0,0) + \frac{1}{2} \vec{x}^T H \vec{x} + \cdots .\end{align*}
Thus $H$ being positive definite is precisely the right thing to check for showing that $f(x,y) > f(0,0)$ in a neighborhood of $(0,0)$ and thus we have a local minimum at our critical point. 
