Math notes / Algebra
This is more for overview of my own than for teaching or exercise.
Other data analysis, data summarization, learning

Contents
Elementary algebra
Elementary algebra or basic algebra are terms that frequently point at the parts of algebra regularly taught in secondary education, typically the more easily understood parts and may be useful to calculus.
To some degree, the concept of basic algebra seems to exist to contrast it with the more complex stuff typically left until university (though basic algebra courses tends to introduce these concepts only explored later).
Algebra assumes knowledge of arithmetic, and introduces some concepts that are central to like
 the concept of variables
 how to play with expressions and how they are affected by certain changes,
 polynomial equations (linear equation, quadratic equation, etc.)
 factorization, root determination, and such
Linear algebra
Linear algebra studies things like linear spaces (a.k.a. vector spaces) and linear functions (a.k.a. linear maps, linear transforms), systems of linear equations.
...in part because this fairly specific focus has relatively wide application.
A lot of said uses relate to, informally, "when a matrix multiplies a vector, it does something meaningful", and a decent area of linear algebra studies the various useful things that you can do.
Vectors and matrices
This article/section is a stub — probably a pile of halfsorted notes, is not wellchecked so may have incorrect bits. (Feel free to ignore, fix, or tell me) 
Conventions: Dimensions and addressing
Matrix dimensions are mentioned as rowbycolumn.
For example, the following is a 3by4 matrix:
[1 2 3 4] M = [5 6 7 8] [9 8 7 6]
Conventionally, matrices are shown with box brackets (Tte above being one approximation), and vectors with parentheses.
Picking out items is often done with subscript (sometimes superscript).
For example, for that last matrix, M_{3 1} points at the third row, first column, the cell with value 9. Using numbers only really happens in examples, algorithms might do M_{i j}.
Vectors are basically lists. Vectors often imply coordinate vectors in thatdimensional space, but there are other uses.
Vectors can be seen as 1bysomething sizes matrices  row vectors.
Or, less commonly, as somethingby1 matrices  columns vectors.
The preference for one in some contexts (e.g. 3D stuff in computers),
comes mostly from what happens around matrix multiplication.
For a vector you have only one dimension, so v_{4} picks out the fourth element from a vector, either way.
In generic definitions you may see vectors described like:
V = (v_{1}, v_{2}, ..., v_{n})
You may see a matrixstyle notation like:
(a b c)
You may also see a symbolic notation for vectors like:
v = ai + bj (two dimensions) v = ai + bj + ck (three dimensions)
...where a, b, and c refer to variables/values in each dimension, and i, j, and k to the dimensions themselves.
Terminology: Properties of some basic matrices
...and a few common terms to go along, like that the main diagonal[1] of a matrix is the the diagonal across the matrix through the elements with the same row index as column index. (top left to bottom right) (Regularly seen mentioned more for square matrices, but it exists for rectangular matrices as well)
A diagonal matrix[2] is a square matrix which has nonzero elements only in the diagonal, e.g.
1 0 0 0 2 0 0 0 2
A square matrix[3] is one that has as many rows as columns. (A number of operations only make sense on a square and not on a rectangularshaped one  which is often relatively intuitive because of what the operation tries to do, or the because of what the contents represent)
An identity matrix[4] is a square diagonal matrix that has 1 on the main diagonal, 0 elsewhere, for example
1 0 0 0 1 0 0 0 1
Notes:
 when used in matrix multiplication, do not change the matrix they are applied to. For example, for any mbyn matrix A:
I_{m} A = A A I_{n} = A
 (I_{n} is often used to refer to an nbyn identity matrix, so that example was I_{3}. Sometimes it's just I, with size left up to context)
 identity matrices help in certain further definitions, like...
An invertible matrix (also: nonsingular, nondegenerate)
 A square matrix is invertible iff its determinant is nonzero
 A square matrix is invertible if there exists a matrix B so that AB = BA = I
 (also, a square matrix is invertible iff the linear map that the matrix represents is an isomorphism)
 If a square matrix has a left inverse or a right inverse then it is invertible (see invertible matrix for other equivalent statements).
A singular matrix (also: noninvertible, degenerate)
 a matrix is singular iff if its determinant is 0
Being invertible / being nonsigular / having a nonzero determinant is often a useful check
 whether a transformation matrix has one that reverses it (e.g. not when it zeroes something out)
 whether rows/columns are linearly independent (when seeing it as a system of linear equations, whether one can be reduced to 0)
 whether a numeric analysis can
 Getting to more specific matrices
Some operations common to those
Terminology: Wider uses of matrices
As matrices are used for any bookkeeping of nontrivial data, they have many specific uses.
Including:
In linear algebra
 representing certain numerical problems,
 for example, and commonly, the coefficients of a set of linear equations (each row being one equation)
 in part just a data storage thing, but there are some matrix properties/transforms that make sense
 Transformation matrix [5]
 storing linear transforms in matrices
 ...so that matrix multiplication, typically on coordinate vectors, will apply that transformation to that vector
 see e.g. the workings of OpenGL, and various introductions to 3D graphics
 Some singlepurpose transformation matrix examples:
 Rotation matrix  [6]
 Shift matrix  http://en.wikipedia.org/wiki/Shift_matrix (ones only on the subdiagonal or superdiagonal)
 Shear matrix  [7]
 Centering matrix  [8]
In graph theory and such
 distance matrix  distances between all given points. E.g. used for graphs, but also for other things where there is a sensible metric.
 adjacency matrix  [9]
 note that multiplying this with itself will express connections in as many steps (like a distance matrix)
 further matrices assisting graph theory, including degree matrix[10], incidence matrix[11],
 Similarity matrix [12]
 Substitution matrix [13]
 Stochastic matrix 
 a.k.a. probability matrix, transition matrix, substitution matrix, Markov matrix
 stores the transitions in a Markov chain
 [14]
In multivariate analysis, statstical analysis, eigenanalysis
 Covariance matrix (a.k.a. autocovariance matrix, dispersion matrix, variance matrix, or variance–covariance matrix)
 used in multivariate analysis
 Stores the covariance between all input variables
 http://en.wikipedia.org/wiki/Covariance_matrix
Other
 Confusion matrix
 a visualisation of the performance of a classification algorithm
 rows are predicted class, columns are known class
 numbers are the fraction of the cases categorized that way, with the diagonal being '...correctly'
 the closer this is to an identity matrix, the better the classifier performed
 representing differential equations
 Many others, see e.g. http://en.wikipedia.org/wiki/List_of_matrices
>
Common concepts around linear algebra
Space
Linear combination
Span, basis, and subspace
Orthogonality, orthonormality
Simultaneous equations / systems of equasions
Linear independence; Basis
Rank
Rank is the maximum number of linearly independent rows that appear in a given matrix.
(Side notes: the rank is at most the minimum of the row and columns size)
http://en.wikipedia.org/wiki/Rank_(linear_algebra)
Angle between two vectors
More complex concepts around here
Eigenvectors and eigenvalues
Eigenvalue algorithms
Power method / power iteration
Deflation Method
Eigendecomposition
Applications
SVD
On the decomposed matrices' sizes
Definition / main properties
In more detail
Further properties, observations, and uses
See also
Others
>
Abstract algebra
Abstract algebra studies the possible generalizations within algebra.
It concerns concepts like group theory, rings, fields, modules, vector spaces, and their interrelations.