LEARNING OUTCOMES
You will learn to identify an ill-posed inverse problem and to understand the restrictions its nature imposes on the solution process. You will familiarize yourself with several classical regularization methods for finding approximate solutions to linear ill-posed problems. You will learn to formulate an inverse problem as a Bayesian problem of statistical inference and to interpret the information contained in the resulting posterior probability distribution. You will learn to numerically implement the introduced solution techniques.
Credits: 5
Schedule: 24.02.2025 - 04.04.2025
Teacher in charge (valid for whole curriculum period):
Teacher in charge (applies in this implementation): Nuutti Hyvönen
Contact information for the course (applies in this implementation):
CEFR level (valid for whole curriculum period):
Language of instruction and studies (applies in this implementation):
Teaching language: English. Languages of study attainment: English
CONTENT, ASSESSMENT AND WORKLOAD
Content
valid for whole curriculum period:
The course s topic is computational methods for solving inverse problems arising from practical applications. The course consists of two parts: the first three weeks focus on classic regularization techniques, the latter three weeks discuss statistical methods.
Assessment Methods and Criteria
valid for whole curriculum period:
Teaching methods: lectures, exercises and home exam.
Assessment methods: exercises, a home exam.
Workload
valid for whole curriculum period:
Contact hours 36h (no compulsory attendance)
Self-study ca 100h
DETAILS
Study Material
valid for whole curriculum period:
All essential material is included in the lecture notes that are available at the course's homepage.
Substitutes for Courses
valid for whole curriculum period:
Prerequisites
valid for whole curriculum period:
FURTHER INFORMATION
Further Information
valid for whole curriculum period:
Teaching Language: English
Teaching Period: 2024-2025 Spring IV
2025-2026 Spring IV