Topic outline

  • General

    The preferable way to register is via WebOodi. Alternatively you can just show up at the first lecture at 14-16 on Monday, February 25 in M3 (M234). The first exercise session is held at 14-16 on Friday, March 1 in M2 (M233).

    The students are assumed to participate actively in the course by weekly returning their solutions to one home assignment (typically involving MATLAB computations). 25% of the overall grade is based on the home assignments and 75% on a home exam.

    The preliminary weekly timetable is as follows:

    • Week 1: Motivation and (truncated) singular value decomposition
    • Week 2: Morozov discrepancy principle and Tikhonov regularization
    • Week 3: Regularization by truncated iterative methods
    • Week 4: Motivation and preliminaries of Bayesian inversion, preliminaries of sampling
    • Week 5: Prior models, Gaussian densities, MCMC (Metropolis-Hastings algorithm)
    • Week 6: MCMC (Gibbs sampler), hypermodels