PE2-2.1 MATLAB Fundamentals & Linear Algebra

Work through these

• Basics of MATLAB; data types and floating point numbers

  The environment the whole course runs in, including how numbers are actually stored. Floating point behaviour matters here because you will be checking results against theory.

• Scripts and flow of control; the for loop

  Writing something longer than one line, and the loop construct. Worth learning the vectorised alternative early, because it is faster and reads better.

• Arrays and indexing

  The data structure everything else uses. Indexing fluently is the difference between fighting the language and thinking in it.

• Results from linear algebra; matrix multiplication

  The linear algebra the rest of the course assumes, exercised in code. Doing it computationally reinforces the theory rather than replacing it.

• Eigenvalues and eigenvectors

  The vectors an operation leaves in place, computed rather than derived. They come back in the transform and detection topics later.

• Complex numbers and Hermitian matrices

  Complex arithmetic and the matrix conjugate transpose, which is what signal processing actually uses. Confusing transpose with conjugate transpose is a bug that produces plausible wrong answers.

• Signals and convolution in MATLAB

  Representing a signal and filtering it, which is where the course turns from mathematics to communication. Convolution here is the operation everything downstream depends on.