Course home page

  • Course home page

    Place and time:
    R037/1023-2014 (TUAS), Lectures Wednesdays 12:15-14:00, Exercises 14:15-15:00. Course starts with a lecture on Wednesday 27.1. at 12:15.

    Lecturer:
    Prof. Simo Särkkä (simo.sarkka@aalto.fi).

    Course assistants:

    M.Sc. Arno Solin (arno.solin@aalto.fi) and Dr. Roland Hostettler (roland.hostettler@aalto.fi)

    Please add "ELEC-E8105" to subject when sending mail concerning the course.

    Learning Outcomes: The student understands the Bayesian basis of estimation in non-linear and non-Gaussian systems. The student understands the principles behind approximate filters and smoothers, and is able to use them in practice. Student knows how to estimate parameters online and offline in non-linear systems.

    Contents: Statistical modeling and estimation of non-linear and non-Gaussian systems. Bayesian filtering and smoothing theory. Extended Kalman filtering and smoothing, sigma-point and unscented filtering and smoothing, sequential Monte Carlo particle filtering and smoothing. Adaptive non-linear filtering; ML, MAP, MCMC, and EM estimation of system parameters. Example applications from navigation, remote surveillance, and time series analysis.

    Assessment Methods and Criteria: Final exam, home exercises, and project work.

    Study Material: Särkkä: Bayesian Filtering and Smoothing (2013), handouts.

    Course Homepage: https://mycourses.aalto.fi/course/view.php?id=11330

    Prerequisites: Basics of Bayesian inference, multivariate calculus and matrix algebra. Basic knowledge or ability to learn to use Matlab or Octave is needed for completing the exercises. "ELEC-E8104 Stochastic models and estimation" is recommended, as well as "BECS-E2601 Bayesian data analysis".

    Grading Scale: 0-5

    Language:
    The course will be taught in English in spring 2015.

    Schedule:
    The lecture/exercise schedule below is preliminary and might change during the course:

    • 27.1.  Overview of Bayesian modeling of time-varying systems
    • 3.2. From linear regression to Kalman filter and beyond
    • 10.2. Bayesian optimal filtering equations and the Kalman filter
    • 24.2. Extended Kalman filter, statistically linearized filter and Fourier-Hermite Kalman filter
    • 2.3. Unscented Kalman filter, Gaussian Filter, GHKF and CKF
    • 9.3. Particle filtering
    • 16.3. Bayesian optimal smoother, Gaussian and particle smoothers
    • 30.3. Bayesian estimation of parameters in state space models
    • 13.4. Recap of the course topics and project work information
    • 13.4.-13.5. Individual project work
    • 25.5. Examination

    Recall that after each lecture (except the first one), starting at 14:15, there is an exercise session where you should attend as well.
    Grades

    The project grades can be downloaded here.

    The exam 25.5.2016 results are available here.

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