This page contains the program of the course. Other information, such as learning outcomes, teachers, literature and examination, can be found in a separate course PM.

Program

The schedule of the course is in TimeEdit.

The course typically has two lectures and two computer exercises each week. Details for these are given in the schedule below, which will be updated during the course. For each lecture, the chapters covered in the books will be listed.

We have the following books and acronyms:

• MvL: Theory of Spatial Statistics: A Concise Introduction, M.N.M. van Lieshout (found here)
• BC: Foundations of Computational Imaging: A Model-Based Approach, C. A. Bouman (found here)
• LN: Lecture notes Statistics of Imaging, M. Rudemo (found here)

Other relevant literature (which might be used in the course):

• HS: Handbook of spatial statistics, Gelfand, Diggle, Guttorp & Fuentes (found here)
• EL: The Elements of Statistical Learning, Hastie, Tibshirani & Friedman (found here)
• GH: Image Analysis for the Biological Sciences, Glasbey & Horgan (found here)

Note that all literature is freely available via the Chalmers library for registered students.

The lectures will be given in Euler.

Projects and examination

The deadlines for the projects can be found at the end of this page under Course summary. The reports should be uploaded in Canvas/Assignments.  

In addition to the projects, there will be a final exam on June 3 2026, 14:00-18:00.
Grade 3/G: 20 p, grade 4: 30 p , grade 5: 40 p, grade VG: 37.5 p, max: 50 p

Lectures and Exercises

Teachers of the course are Ottmar Cronie (lectures, examiner), ottmar@chalmers.se, and Mathis Rost (lectures, exercises/labs), mathisr@chalmers.se. Lecture notes of the lectures can be found in Files/Lecture notes. The information below will be updated and extended on an ongoing basis.

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