Course in 2016
The course will be arranged by Prof. Simo Särkkä (email@example.com) in Period II (starting in the end of October 2016) with the topic Applied Stochastic Differential Equations (3 credits). The course will be arranged at the same time in Tampere University of Technology, and there the course organizer is Prof. Robert Piché (firstname.lastname@example.org).
Registration: Aalto students should register via Oodi, and registration information for TUT students can be asked from Robert Piché or Simo Särkkä.
The first contact session on Thursday November 3nd at 14:15-16:00, which in Aalto University side is in room 1021-1022, TUAS, Maarintie 8. In Tampere of University Technology side the contact sessions, including the first one, are exactly the same time in room Sc209, and a video link is arranged between the universities.
A compulsory pre-assignment is required before the first contact session and it will be announced one week before the first contact session.
The course will be arranged as a flipped class type of course:
Lectures are delivered as Youtube videos (will be linked in Materials section) which are independently studied by the students before the exercise sessions. Quizzes attached to the lectures should be completed as well.
Exercise sessions are organized as contact teaching where a lecturer is present at the room (Simo Särkkä at Aalto and Robert Piché at TUT), and the Aalto and TUT sessions are connected via Adobe Connect. A remote attendance is possible as well. The AC link is: https://connect.funet.fi/aalto_tut_sde_2016/
Homework exercises must be completed before the exercise sessions (see Assigments section for information). At least 50% (9/18) of homework exercises must be completed and completing at least 80% (15/18) of exercises gives a +1 grade increase
Homework exercises can also be independently done and returned in PDF form to MyCourses before the exercise sessions.
Project work is used to grade the course. The project work topic should be selected by 8.12.2016. The deadline of the project work is 8.1.2017.
Description of the topic
Topic: The course is an introduction to stochastic differential equations (SDEs) from an applied point of view. The contents include the theory, applications and numerical methods for SDEs. Examples of SDE models are given in mechanics and electrical engineering, physics, target tracking, and machine learning. The course uses flipped classroom distance teaching: before each session, students follow online prerecorded lectures, read textbook sections, and work on homework problems. In the exercise sessions, students present their solutions and the teacher provides comments.
Target audience: Advanced undergraduate and graduate (PhD) students. Researchers and engineers wishing to get a hands-on introduction to the topic.
Prerequisites: Multivariate differential and integral calculus, matrix analysis, basic probability, Matlab/Octave.