What is the theory of Matrices? I've just passed high school and studying matrices. I've learned about determinant, transpose and adjoint etc. I've learned the method of finding these things but what's the purpose of finding these things? What actually Matrices do which makes it solving equations easier. 
Why determinant is equal to $(ab)-(cd)$ not $(cd)-(ab)$ for the matrix, $\left[\begin{matrix}a&c\\d&b\end{matrix}\right] $ ?
Why inverse of $\rm A$ is equal to $\dfrac{\operatorname{adj}A} {\det A}$ not $\dfrac{\det A} {\operatorname{adj}A}$ ?
I've read many answers like, 
What is the usefulness of matrices? , they say that matrices do this and that ,but they does not explain how or why ?
Also what is the relation between vectors and matrices?
 A: Matrix theory is linear algebra with the method of the coordinate systems.
As to why the determinant is calculated that way try to compute the area of a square of unitary length side once it is transformed by a matrix (considering two adjacent sides as vectors). Determinant is an operation that can be applied to any linear operator $L: A\rightarrow A$, $A$ is a linear space over a field, and it gives an element of such a field and has such a geometrical interpretation that I asked you to search for.
"Taking the inverse" is again an operation that can be applied to any linear operator that has a non-null determinant.
Adjoint: Given a square matrix the sum of the product of the elements of a row (column) and the corresponding cofactors equals the determinant, while the sum of the product of the elements of a row (column) and the corresponding cofactors of the elements of another row (column) is null. That means that:$$A \operatorname{adj}(A)=\det A$$ where with $\det A$ I mean a diagonal matrix whose elements are all equal to the determinant. 
Transpose is an operation that can be applied to any linear application $L: A\rightarrow B$, where $A$ and $B$ are linear space over the same field $\Bbb{K}$: it gives another linear application that describes how scalar linear functional on $B$, $f:B\rightarrow \Bbb{K}$, are mapped into scalar linear functional on $A$, $g:A\rightarrow \Bbb{K}$, as a composition of $f$ with $L$: $g=f\circ L$. In this way instead of performing such computation $f(L(x))$ one can simply do $g(x)$, for any $x\in A$
Matrices in addition to linear application can also be used to describe bilinear and quadratic forms in given coordinate systems.
Vectors are the element of a linear space. Coordinate vectors are their representation once a linear coordinate system has been chosen for such a linear space. With a linear coordinate system a linear space $A$ over a field $\Bbb{K}$ becomes an homomorphic image of a (coordinate) linear space $\Bbb{K}^n$, $n\in\Bbb{N}$ where $n\ge dim_\Bbb{K}A$. If the linear coordinate system is bijective the equality holds. So coordinate vectors are elements of the coordinate linear space. What you have being using till now are coordinate vectors even though you have been calling them simply vectors. By extension usually the numerical representation of a vector is called coordinate vector also when the coordinate system chosen is not linear, but in this case they cannot be used with the algorithm of the matrix theory. Coordinate vectors are also used to represent numerically linear functional once a linear coordinate system is chosen: those are usually represented as row vectors, while the former are represented as column vectors.
A: Matrix theory can be viewed as the calculational side of linear algebra.  Linear algebra is the theory of vectors, vector spaces, linear transformations between vector spaces, and so on, but if one wants to calculate particular instances, one uses matrix algebra.  In part it is a body of notational conventions for how one represents the abstractions described by linear algebra, and in part a collection of recipes for manipulating these notations.
The boundary between MA and LA is not crisp, and a case can be made that there are topics in MA that are not really discussed in LA, so my description above is perhaps simplistic.  In particular,  the Perron-Frobenius theorem  seems inherently bound to matrices and don't seem to have clean abstract linear algebra formulation.  Similarly the subject of "total positivity".
A: *

*A multiplicative inverse to $A$ is a matrix that when you multiply it with $A$ becomes the identity matrix. That is the definition of inverse.

*You can define matrix multiplication or division by scalar, but det./adj would be a scalar divided by matrix and it is in general not defined.

*The usefulness of matrices is everywhere. There is today definitely no branch of science or engineering where you will not find use for matrices. But it is difficult to explain why before you learn more about them!

A: I'll take a very... non-Bourbakian approach on this.
Matrices are transformations! You do have an intuition what vectors are? The very first intuition is: if you want to transform your vectors from one coordinate system ("base") to another, you'd need something encoding this transformation. A matrix.
Of course, there is much more to it. We can encode large linear equation systems as matrix-vector product. So, solving these equations is reshuffling the matrix, basically. Think: $Ax=b$, with matrix A and vectors $x$ and $b$, can be solved for $x$ when we manage to invert the matrix $A$. But that's a very crude approach, look up LU decomposition.
Pretty things happen when you study the matrix-vector product, you reach bilinear forms $v^tAv$, scalar products and other funny stuff.
Another issue that might interest you are all the transformations you do (with matrices!) in computer graphics! They basically transform the 3D world representation to the viewport of your screen with a $\mathbb{R}^4$ matrix-vector product. Why four-dimensional? Read up, cannot post a link for a stupid reason.
Oh, and you can generalize matrices to tensors, basically $n$-dimensional matrices, if matrices are 2D.
To conclude: linear algebra is pretty as it is, but it is first of all a tool of a working mathematician. You use it to solve other problems. For example, if you can reduce your problem to a (even very large) linear equation system, you are done.
A: Matrix computations with matrices (multiplication, and inversion)  are very much used in the optimization of of multivariate functions under additional constraints.They speed up optimization tasks enormously especially with high dimensional problems. Optimization is a the tool for using machine learning algorithms to teach computers (machine) to solve problems without given them the instructions to do so, and without human intervention. Machine learning is one important branch of Artificial Intelligence (AI) that's helping automate many difficult but useful tasks such as hand writing and face recognition, fraud detection, medical diagnostics and self driving cars.
A: Be patient.  There are many wonderful ideas that come out of matrix theory.  The elements of matrices don't have to be real numbers; they can be elements of a finite field, or they can be imaginary or complex numbers.  You can do more than just add and multiply matrices; you can get their sines, arctangents, logarithms, square roots, and more.  There is a whole branch of computing where special methods are studied to accurately compute the inverse.   In probability theory and reliability engineering, matrices represent transition probabilities of a piece of equipment from good to various failed states.  Anther thing you can do with matrices is linear programming: finding the best solution for a system subject to constraints.   And there is much, much more.
So be patient.  You will come to appreciate and love matrices.  For now, I advise you to force yourself to learn everything your math class has to offer about matrices; if you do this you will never be sorry.
A: 
I've just passed high school and studying matrices. I've learned about
  determinant, transpose and adjoint etc. I've learned the method of
  finding these things but what's the purpose of finding these things?

First it is a useful way of aggregating several objects. A way of organizing. A matrix
$$
A = (a_{ij}) \quad i \in \{1, \dotsc, m \}, j \in \{ 1, \dotsc, n \} \quad m, n \in \mathbb{N}
$$
will allow you to treat $m \times n$ objects under the handle $A$. Or
$$
x = (x_i) \quad i \in \{1, \dotsc, m \} \quad m \in \mathbb{N}
$$
a (column) vector, which can be interpreted as $m \times 1$ matrix, allows you
treat $m$ objects under the handle $x$.
Up to here we could have also written this using functions:
$$
a_{ij} = a(i, j) : \{1, \dotsc, m \} \times \{1, \dotsc, n \} \to X \\
x_i = x(i) : \{1, \dotsc, m \} \to X
$$
This already has applications, e.g. in a programming language we might find this implemented as data structures (a useful way to handle data), as arrays or e.g. as hash maps, if we index with elements from other sets than whole numbers.
These aggregates become even more useful if one can operate on them and it turns out that they are useful to describe nature in physics, even in the versions with more than two indices and indices ranging over infinite sets.
(To be continued later)

What actually Matrices do which makes it solving equations easier.
Why determinant is equal to $(ab)-(cd)$ not $(cd)-(ab)$ for the
  matrix, $\left[\begin{matrix}a&c\\d&b\end{matrix}\right] $ ?
Why inverse of $\rm A$ is equal to $\dfrac{\operatorname{adj}A} {\det
> A}$ not $\dfrac{\det A} {\operatorname{adj}A}$ ?
I've read many answers like, 
  What is the usefulness of matrices?
  , they say that matrices do this and that ,but they does not explain
  how or why ?
Also what is the relation between vectors and matrices?

