Statistics: Bayes Rule, Priors, Posteriors, Maximum Likelihood Principle (MLE), Basic distributions such as Bernoulli, Binomial, Multinomial, Poisson, Gaussian. Multivariate versions of these distributions, especially Multivariate Gaussian Distribution. (refresher, reference sheet)
Linear Algebra: Vector spaces, subspaces, matrix inversion, matrix multiplication, linear independence, rank, determinants, orthonormality, basis, solving systems of linear equations. Eigenvectors/values, Eigen- and Singular Value Decomposition. Identifying and working with popular types of matrices - eg symmetric matrices, positive (semi-) definite matrices, non-singular matrices, unitary matrices, rotation matrices, etc. (refresher 1, refresher 2, refresher 3, refresher 4)
Multivariate Calculus: Take derivatives and integrals of common functions, gradient, Jacobian, Hessian, compute maxima and minima of common functions. Differentiation of vector valued functions. (basic calculus identities, multivariable differentiation, extrema refresher, refresher 1, refresher 2)
Programming: Ability to program in a high-level language, and familiarity with basic algorithm and datastructure design and good coding principles.
Please include your name and UNI on the first page of the written assignment and at the top level comment of your programming assignment.
The written segment of the homework (including plots and comparative experimental studies) must be submitted via Gradescope, and (if the homeworks specifies) the a tarball of the programming files should be handed to the TA by the specified due dates. No late homeworks will be accepted.