Topic outline

  • General

    Although ordinary lectures are given according to the published timetable (apart from 1 March due to travel), the lectures have also been prerecorded and published at the Materials section of the course's MyCourses homepage. The first lecture is on 26 February at 14.15–16.00 in M3.

    A weekly exercise session is held on Friday at 14.15–16.00 in classroom M2 (M233) starting on 1 March.

    There are no lectures or exercise sessions during the Spring break 28 March – 3 April 2024.

    The students are assumed to participate actively in the course by weekly returning their solutions to one home assignment that typically involves MATLAB computations. 25% of the overall grade is based on the home assignments and 75% on a home exam that is held after the lectures have ended during 15 April – 13 May. Solving the exam problems and reporting their solutions takes 15–40 hours according to data from previous years.

    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

    Course staff:
    Nuutti Hyvönen (lecturer; nuutti.hyvonen "at" aalto.fi)
    Pauliina Hirvi (teaching assistant; pauliina.hirvi "at" aalto.fi)