Course syllabus
This page contains the program of the course: lectures and computer labs. Other information, such as learning outcomes, teachers, literature and examination, are in a separate course PM.
Program
The schedule of the course is in TimeEdit.
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 notes and HS denotes the Handbook of spatial statistics and EL The Elements of Statistical Learning. There are also handouts from a previous course available in the folder Old handouts under Files.
Projects and examination
The deadlines for the projects can be found in the end of this page under Course summary. In the PM, it says that you should email your project reports to Konstantinos and Aila but you can also upload them in Canvas (in which case no email is needed).
In addition to the projects, there will be a final exam on June 2 at 14:00-18:00 by Zoom. You can use any aid in terms of literature and computers but you are not allowed to communicate with other people in any way.
Due to Corona virus all teaching will be performed remotely on Zoom using the following links:
Lectures: https://chalmers.zoom.us/j/61569881002 Password: 642830
Project presentations (the same as for Lectures): https://chalmers.zoom.us/j/61569881002 Password: 642830
Computer exercises: https://chalmers.zoom.us/j/66768662070 Password: 411422
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Lectures and Exercises
Teachers of the course are Aila Särkkä (lectures, examiner), aila@chalmers.se, and Konstantinos Konstantinou (lectures, computer exercises), konkons@chalmers.se. Lecture notes and recordings of the lectures can be found in Files/Lecture notes. The information below will be updated and extended on an ongoing basis.
| Time | Lecture | Computer exercise | ||
|---|---|---|---|---|
| 22 mars | Monday 10:00-11:45 |
L1 |
Introduction, lecture notes and LN pages 1-24, see also pages 1-8 of Glasbey and Horgan 1995, Chapter 5. |
|
| Monday 13:15-15:00 |
E1 | Basic image processing | ||
| 24 mars | Wednesday 10:00-11:45 |
L2 | Spatial random processes, lecture notes and LN pages 69-78. | |
| Wednesday 13:15-15:00 |
E2 | Gaussian fields | ||
| 12 april | Monday 10:00-11:45 |
L3 |
OLS, GLS and ML estimation, lecture notes and LN pages 78-85. |
|
| Monday 13:15-15:00 |
E3 | Estimation and kriging | ||
| 14 april | Wednesday 10:00-11:45 |
L4 |
Gaussian Markov random fields, LDA, QDA, image moments, lecture notes, HS 12.1.1-12.1.4, see also LN pages 31-38 and EL 4.3 |
|
| Wednesday 13:15-15:00 |
E4 | Continue working on exercise 3 | ||
| 19 april | Monday 10:00-11:45 |
L5 |
K-fold cross validation, m-nearest neighbors, SVM, neural networks, lecture notes, LN pages 38-42, 53-59 and EL 7.10, 2.3.2, 12.2-12.3.2, 11.3-11.5 |
|
| Monday 13:15-15:00 |
E5 | |||
| 21 april | Wednesday 10:00-11:45 |
L6 |
Image segmentation, Gaussian mixture models, k-means, morphological operations, feature extraction, lecture notes. LN 1.3.1, 1.5-1.6, 2.8. See also EL 8.5.1, 13.2.1, 13.2.3, 14.3.6-14.3.7. |
|
| Wednesday 13:15-15:00 |
E6 | Image classification | ||
| 26 april | Monday 10:00-11:45 |
L7 | Markov random fields, lecture notes and LN pages 60-67 | |
| Monday 13:15-15:00 |
E7 | Image segmentation using mixture models | ||
| 28 april | Wednesday 10:00-11:45 |
L8 | Point process analysis, lecture notes LN pages 86-93. | |
| Wednesday 13:15-15:00 |
E8 | Simulation of MRFs | ||
| 3 maj | Monday 10:00-11:45 |
L9 |
Marked point processes, point processes with noise, lecture notes and LN pages 94-96 and 117-129. |
|
| Monday 13:15-15:00 |
E9 | Estimation and classification using MRFs | ||
| 5 maj | Wednesday 10:00-11:45 |
L10 | Applications of point processes/image analysis to nerve fiber data, lecture notes |
|
| Wednesday 13:15-15:00 |
E10 | Point processes | ||
| 10 maj | Monday 10:00-11:45 |
L11 | Guest lecture on analysing microscopy (FIB-SEM, FRAP, and particle tracking) data by Magnus Röding, RISE, lecture notes | |
| Monday 13:15-15:00 |
E11 | Work on projects | ||
| 12 maj | Wednesday 10:00-11:45 |
L12 | Analysing microscopy data by RICS and SPRIA, lecture notes and LN pages 145-153 | |
| Wednesday 13:15-15:00 |
E12 | Work on projects | ||
| 17 maj | Monday 10:00-11:45 |
L13 | Project seminars | |
| Monday 13:15-15:00 |
E13 | Project seminars in room |
||
| 19 maj | Wednesday 10:00-11:45 |
L14 | Project seminars | |
| Wednesday 13:15-15:00 |
E14 | Project seminars in room |
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 summary:
| Date | Details | Due |
|---|---|---|