Abstract:

The first part of the course is dedicated to study of techniques for

solving systems of linear equations. Several basic methods are presented:

Gauss elimination, matrix solution, Cramer rule.

The last two methods involve matrix arithmetic, which is also presented in this part. The second part discusses vector spaces and linear transformations. Most attention is given to the matrix approach. Matrix diagonalization issues summarize this part.

Abstract:

Basic concepts in probability theory: sample space, elementary theorems, combinatorial calculations, conditional probability and independence, random discrete variables, expected value and variance, special random variables,