Calculus
  - derivatives, integration
 
Multivariate calculus
  - gradient
  - Hessian

Limits of functions
  - definitions of convergence

Big O() notation, such as O(log(n))

Advanced Linear Algebra
  - eigenvalues, eigenvectors
  - orthogonal matrices
  - symmetric matrices - properties of eigenvalues
    	      	       - properties of eigenvectors
		       - positive definiteness
		       - covariance matrices
  - (linear) subspace, basis of a subspace
  - projection on a subspace, projection matrix
  - hyperplane, normal to a hyperplane
  - linear functions
  - scalar product, Euclidean scalar product x'*y, orthogonal vectors
  - norms of vectors: 2-norm, 1-norm, infinity-norm
  - norms of matrices: 2-norm
  - determinant, trace
  - condition number of a matrix
  - [Riesz representer theorem]


Probability and statistics
  - consistency
  - central limit theorem
  - Binomial, Multinomial, Gaussian, [also useful multivariate Gaussian, Poisson, ... ]distributions

   We will study these
  - Maximum Likelihood, Bayesian estimation paradigms
  - empirical distribution
  - [change of variable formula in integration]