Random variable, probability density function, expectation, moments.
Gaussian random variable.
Independent variables. Conditional probability density function.
Bayes' rule (reviewed in class).
Linear algebra
Vector, matrix, product.
Basis of a linear space. Change of basis.
Eigenvalues, eigenvectors.
Representing rotations and other linear transformations with matrices.
Singular value decomposition (reviewed in class).
Signal processing and linear systems
Convolution, correlation.
Matlab (will be used a lot for homeworks !)
Some familiarity. Make sure that you have a computer account from which
you can run Matlab (e.g. ITS).
Matrix-vector product. Plot a function. Load and display an image.
Run convolution.
Highly recommended to go through the brief tutorial.
Book
No book is required for the class. Slides or notes of the lectures will be available on-line after the class.
However, people who are interested in computer vision may want to purchase the following reference book:
Computer Vision, by Forsyth and Ponce.
No book is required for the class. Slides or notes of the lectures will be available on-line after the class.