Course in 2018
The course will be arranged by Profs. Simo Särkkä (firstname.lastname@example.org) and Arno Solin (email@example.com) in Period II (starting in the end of October 2018) with the topic Applied Stochastic Differential Equations (3 credits). In addition to the present EEA-EV course code, it is also possible to get credits on a CS-EV course code. Course assistant is Zheng Zhao (firstname.lastname@example.org).
The contact sessions are on Tuesdays at 14:15-16:00 in room TU7 (TUAS 1200-1201). The first contact session is on 30.10.
Note that the students should watch the first video and complete the first homework exercise before or latest in the day of 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 contact sessions. Quizzes attached to the lectures should be completed as well and completing all of them gives 1 extra homework exercise point.
- Contact sessions are organized as contact teaching where a lecturer is present at the room (Simo Särkkä or Arno Solin). The contact sessions are discussion sessions about the current lecture and the homework.
- Homework exercises must be completed preferably before the contact sessions (see Homework exercises and contact sessions section for information) and they must be returned via mycourses during the day of the contact session (except first). 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.
- Project work is used to grade the course. The project work topic should be selected by x.12.2018 (TBA). The deadline of the project work is x.1.2019 (TBA).
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 contact sessions, the current lecture topic and the homeworks are discussed.
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/Python.