Topic outline

  • General

    Course topic, target audience, and prerequisites

    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 and/or read textbook sections, and work on homework problems. In the virtual 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.

    Course in Summer 2020

    The course will be arranged by Profs. Simo Särkkä (simo.sarkka@aalto.fi) and Arno Solin (arno.solin@aalto.fi) in summertime 1.7.-28.8.2020 and the exam is on week 31.8.-4.9. The course assistant is Zheng Zhao (zheng.zhao@aalto.fi).

    The course is fully online although virtual sessions contact sessions are organized via course Slack team weekly. The virtual contact sessions are organized weekly on Wednesdays 14:15-16:00. The first contact session is 1.7. at 14:15-16:00. 

    The course will be arranged as a flipped class type of course:

    • The coursebook is available online here: http://users.aalto.fi/~ssarkka/pub/sde_book.pdf and the example codes from the book here: https://github.com/AaltoML/SDE

    • Lectures are delivered as YouTube videos (will be linked in the Materials section) which are independently studied by the students before the contact sessions. Some of the lectures are delivered in the form of independent reading instructions instead of videos.

    • Lecture Slides are also available in the Materials section.

    • Quizzes attached to the lectures should be completed as well and completing all of them gives 1 extra homework exercise point. However, the first quiz is mandatory.

    • Contact sessions are organized in Slack such that a lecturer is present at the slack room (Simo Särkkä, Arno Solin, or Zheng Zhao). The contact sessions are discussion sessions about the current lecture/material 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 in two batches: rounds 1-4 on 31.7. and rounds 5-8 on 28.8. At least 50% (12/24) of homework exercises must be completed and completing at least 75% (18/24) of exercises gives a +1 grade increase to the exam grade. 

    • The project work topic should be selected by 26.7.2020 (report it here in MyCourses). The deadline of the project work is 28.8.2020 (return it in MyCourses).

    • The course exam will be organized in the week 31.8.-4.9. as an online exam (details communicated directly to attendants, let us know if you didn't get the details). 

    • The grade of the course is the maximum of the exam grade and the project work grade. However, you must get a passing grade from both the exam and project work to pass the course.