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A Computational Introduction to Stochastic Differential Equations2025HT, 6.0 credits
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Course plan
No of lectures
The course will take place between 03.11.2025 and 06.11.2025, running
intensively in 4 days.
The detailed syllabus and course plan is shown at
https://github.com/spdes/computational-sde-intro-lecture/.
Recommended for
PhD students who have an applied or engineering background, e.g., machine
learning, signal processing, statistics, computer vision, and control.
The course is particularly suitable for students who want to apply generative
diffusion models for their own research.
The course was last given
Uppsala University 2022. It was a success, see the feedback at https://github.com/spdes/computational-sde-intro-lecture/blob/main/annual_reports/2022/Ogranskad%20sammanstallning%20-%20kurt80463_ogranskad.pdf
Goals
This course aims to develop a computational view of stochastic differential
equations (SDEs) for students who have an applied or engineering background,
e.g., machine learning, signal processing, statistics, computer vision, and
control.
**The course is computation-focused not theory**.
Prerequisites
Linear algebra.
Real analysis (not essential, but very useful).
Probability theory.
Ordinary differential equations.
Organization
Content
Mainly focus on:
1. Fundaments of stochastic differential equations (SDEs).
2. Computations (e.g., simulation and statistical quantities) of SDEs.
3. Generative diffusion models.
Literature
6 basic lectures plus seminar lectures.
Lectures
Examination
Exercises.
Examiner
Zheng Zhao
Credits
The basic credit of the course is 6 consisting of lectures and exercises. The student can upgrade to 9 credit, if they additional do a project work and write a report.
Comments
Page responsible: Anne Moe
