Latest news
September 2: Preliminary solutions to the August exam can be found here.
June 7: Preliminary solutions to the exam can be found here.
May 22: There is a new version of the lecture notes by Mats Rudemo available here. If you have any comments or suggestions regarding the lecture notes, you are welcome to send an email to rudemo@chalmers.se.
May 16: The project presentations next week will be in the following rooms: Monday 10-12 and 13-15 in MVF26, Wednesday 10-12 in Euler, and Wednesday 13-15 in MVF31.
April 17: I have fixed some bugs in the matlab files for the course, so make sure to download the zip-file again to get the correct version.
April 2: The PDF for Lab 3 has been updated to clarify how the data should be generated. I have also fixed a bug in one of the matlab files for the course, so make sure to download the zip-file again to get the correct version.
Welcome to the course! The schedule for the course can be found in TimeEdit.
Teachers
Course coordinator: David Bolin (david.bolin@chalmers.se)
Course literature
Most of the material covered in the course is briefly described in the Lecture notes (abbreviated LN in the schedule below) by Mats Rudemo.
During the first part of the course, we will mainly use chapters from the Handbook of Spatial Statistics, which is available as an eBook through the Chalmers library. For the second part of the course, we will also use material from
The Elements of Statistical Learning, which also is available as an eBook through the Chalmers library.
Program
The course 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 the lectures, the chapters covered in the books are listed, where LN denotes the lecture nodes and HS denotes the Handbook of spatial statistics and EL The Elements of Statistical Learning.The lectures will be in room MVF26 on Mondays and in Euler on Wednesdays. The computer exercises are always in room MVF25.
| Time | Lecture | Computer exercise | ||
|---|---|---|---|---|
| Week 1/13 | Monday 10:00-11:45 |
L1 |
Introduction
LN pages 1-16. |
|
| Monday 13:15-15:00 |
E1 | Basic image processing | ||
| Wednesday 10:00-11:45 |
L2 |
Random fields
HS 2.1-2.7 |
||
| Wednesday 13:15-15:00 |
E2 | Gaussian fields | Week 2/14 | Monday 10:00-11:45 |
L3 |
Estimation and kriging
HS 2.8, 3 |
| Monday 13:15-15:00 |
E3 | Estimation and kriging | ||
| Wednesday 10:00-11:45 |
L4 |
Likelihood-based parameter estimation
HS 4.1-4.3 |
||
| Wednesday 13:15-15:00 |
E4 | Continue working on exercise 3 | Week 3/15 | Monday 10:00-11:45 |
L5 |
Gaussian Markov random fields
HS 12.1.1-12.1.4, parts of 12.1.7, and 13.1-13.2 |
| Monday 13:15-15:00 |
E5 | Image reconstruction using GMRFs | ||
| Wednesday 10:00-11:45 |
L6 |
Image segmentation and mixture models
LN 1.3, 2.1-2.3,2.8 |
||
| Wednesday 13:15-15:00 |
E6 | Image segmentation using mixture models | Week 4/16 | Monday 10:00-11:45 |
L7 |
Feature selection and filtering
LN 1.2,1.4-1.6 |
| Monday 13:15-15:00 |
E7 | Image filtering | ||
| Wednesday 10:00-11:45 |
L8 |
Discrete Markov random fields
HS 12.1.5, 12.1.8, 12.1.9 |
||
| Wednesday 13:15-15:00 |
E8 | Simulation of MRFs | ||
| Week 5/19 | Monday 10:00-11:45 |
L9 |
Estimation of Markov random fields
LN 4 |
|
| Monday 13:15-15:00 |
E9 |
Estimation and classification using MRFs |
Wednesday 10:00-11:45 |
L10 |
Classification and machine learning
LN 2.4-2.7, 3.2, EL 2, 12 |
| Wednesday 13:15-15:00 |
E10 | Image classification | ||
| Week 6/20 | Monday 10:00-11:45 |
L11 |
Neural networks
LN 3.1, EL 11 |
|
| Monday 13:15-15:00 |
E8 | Work on projects | Wednesday 10:00-11:45 |
L12 |
Point processes
LN 6,7 |
| Wednesday 13:15-15:00 |
E12 | Work on projects | Week 7/21 | Monday 10:00-11:45 |
L14 | Project seminars |
| Monday 13:15-15:00 |
E14 | Project seminars in room MVF26 | ||
| Wednesday 10:00-11:45 |
L15 | Project seminars | ||
| Wednesday 13:15-15:00 |
E8 | Project seminars in room MVF31 | ||
Computer labs
The exercises will be done in Matlab, and some knowledge of Matlab is assumed. If you need an introduction, see Learning MATLAB, Tobin A. Driscoll ISBN: 978-0-898716-83-2 (The book is published by SIAM).
Most computer exercises will use functions written specifically for this course. These are collected in the following file: TMS016_Matlab.zip. Download this file and add the path to the folder in matlab: addpath('path_to_folder'). Then run the command tms016path to set the path to the files.
Data used in the exercies are colleded here: TMS016_Data.zip.
Reference literature:
Learning MATLAB, Tobin A. Driscoll ISBN: 978-0-898716-83-2 (The book is published by SIAM).
Course requirements and representatives
The learning goals of the course can be found in the course plan.
The student representatives for the course are Emil Forslund (emilfo@student.chalmers.se) and Thomas Jonsson Damgaard (thodam@student.chalmers.se).
Assignments
Examination
The examination consists of two parts: One written exam at the end of the course and one project assignment. These two parts are weighted equally for the final grade. The written exam is individual whereas the project work can be done in groups of 1-3 students.
The project consists of three parts:
- Image reconstruction (based on computer exercises 1-4). Images: rosetta.jpg, titan.jpg
- Image segmentation (based on computer exercises 5-8). Data: permeability.mat
- Problem of your own choice.
Regarding the project report:
- Parts 1 and 2 should be documented as lab reports, describing what you did and the results you obtained but it does not need a detailed introduction and discussion.
- The ideal form of the report for Part 3 is in principle a journal paper, containing:
- Project title, author names, course name, date of report.
- Abstract/Summary: about 10-15 lines.
- Introduction: Statement of problem, earlier work with references.
- Data description and source.
- Methods: Mathematical, statistical, computational, image analysis.
- Results with tables and figures.
- Discussion: include if possible here also comparison with results from literature.
- Conclusions, suggested continued studies.
- References.
Deadlines for the project:
- For Parts 1 and 2: A PDF containing these two parts should be submitted via PINGPONG at the latest May 17, 23:55. Include the Matlab code as a zip-file.
- For Part 3: A PDF containing a preliminary version of the report should be submitted together with Parts 1 and 2 at the latest May 17, 23:55. It does not need to be complete. The final version of the report should be submitted at the latest May 31, 23:55, together with the Matlab code as a zip-file, as a revision of the submitted project in PINGPONG.
Examination procedures
In Chalmers Student Portal you can read about when exams are given and what rules apply on exams at Chalmers. In addition to that, there is a schedule when exams are given for courses at University of Gothenburg. Before the exam, it is important that you sign up for the examination. If you study at Chalmers, you can do this from the Chalmers Student Portal, and if you study at University of Gothenburg, you sign up via GU's Student Portal.At the exam, you should be able to show valid identification.
After the exam has been graded, you can see your results in Ladok by logging on to your Student portal.
At the annual (regular) examination:
When it is practical, a separate review is arranged. The date of
the review will be announced here on the course homepage. Anyone
who can not participate in the review may thereafter retrieve
and review their exam at the Mathematical
Sciences Student office. Check that you have the right
grades and score. Any complaints about the marking must be
submitted in writing at the office, where there is a form to
fill out.
At re-examination:
Exams are reviewed and retrieved at the Mathematical
Sciences Student office. Check that you have the right
grades and score. Any complaints about the marking must be
submitted in writing at the office, where there is a form to
fill out.