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 each lecture, the chapters covered in the books are listed. Here, LN denotes the lecture notes (Rudemo), HS denotes the Handbook of spatial statistics (Gelfand, Diggle, Guttorp & Fuentes), EL denotes The Elements of Statistical Learning (Hastie, Tibshirani & Friedman) and GH denotes Image Analysis for the Biological Sciences (Glasbey & Hogan). 

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 May 29 during 14:00-18:00.

Lectures and Exercises

Teachers of the course are Ottmar Cronie (lectures, examiner), ottmar@chalmers.se, and Konstantinos Konstantinou (lectures, computer exercises), konkons@chalmers.se. Lecture notes of the lectures can be found in Files/Lecture notes, here you find LN, here you find HS, here you find EL and GH can be found hereThe information below will be updated and extended on an ongoing basis.

 

Time Room Lecture Computer exercise
March 18 Monday
8:00-9:45
Euler L1

Introduction: LN pages 1-24, see also pages 1-8 of GH, Chapter 5. Lecture notes: LN1_2024.pdf

Teacher: Ottmar

 
  Monday
13:15-15:00
MVF24 and MVF25 E1 Teacher: Konstantinos

Basic image processing

solution

 

March 20 Wednesday
10:00-11:45
Euler L2

Markov random fields: LN pages 60-67. Lecture notes: LN2_2024.pdf

Teacher: Ottmar

 
  Wednesday
13:15-15:00
MVF24 and MVF25 E2 Teacher: Konstantinos

Simulation of MRFs

solution

April 8 Monday
8:00-9:45
Euler L3

Spatial random processes: LN pages 69-78. Lecture notes: LN3_2024.pdf

Teacher: Konstantinos

 
  Monday
13:15-15:00
MVF24 and MVF25 E3 Teacher: Konstantinos

Gaussian fields

solution

April 10 Wednesday
10:00-11:45
Euler L4

OLS, GLS and ML estimation: LN pages 78-85. Lecture notes: LN4_2024.pdf

Teacher: Konstantinos

 

 
  Wednesday
13:15-15:00
MVF24 and MVF25 E4 Teacher: Konstantinos

Estimation and Kriging

solution

April 15 Monday
8:00-9:45
Euler L5

GMRFs: HS pages 172-175, 201-207. Point process analysis (part 1): LN pages 86-93. Lecture notes: LN5_2024.pdfLN5-6_2024.pdf

Teacher: Ottmar

 
  Monday
13:15-15:00
MVF24 and MVF25 E5 Teacher: Konstantinos Continue working on exercise 4

April 17 Wednesday
10:00-11:45
Euler L6

Point process analysis (part 2): LN pages 86-93. Lecture notes: LN5-6_2024.pdf

Teacher: Ottmar

 
  Wednesday
13:15-15:00
MVF24 and MVF25 E6 Teacher: Konstantinos Image reconstruction using GMRFs

April 22 Monday
8:00-9:45
Euler L7

Marked point processes, superpositions and thinnings: LN pages 94-96 and 117-129. Lecture notes: 

Teacher: Ottmar

 
  Monday
13:15-15:00
MVF24 and MVF25 E7 Teacher: Konstantinos Point processes

  DEADLINE FOR PLANNING REPORT PROJECT PART 3

 

April 24 Wednesday
10:00-11:45
Euler L8

LDA, QDA, image moments:  LN pages 31-38 and EL 4.3. 

Cross validation, supervised methods: LN pages 38-42 and 53-59 and sections 2.3.2, 7.10, 12.2-12.3.2, 11.3-11.5 in EL.

Lecture notes: 

Teacher: Konstantinos

 
  Wednesday
13:15-15:00
MVF24 and MVF25 E8 Teacher: Konstantinos

Image classification

April 29 Monday
8:00-9:45
Euler L9

Image segmentation, morphological operations and feature extraction: LN 1.5-1.6, 2.8. See also EL 8.5.1, 14.3.6-14.3.7. Lecture notes: 

Teacher: Konstantinos

 
  Monday
13:15-15:00
MVF24 and MVF25 E9 Teacher: Konstantinos

Image segmentation

May 6 Monday
8:00-9:45
Euler L10

Extra lecture/question session.

Case study on applications of point processes: Ottmar.

 
  Monday
13:15-15:00
MVF24 and MVF25 E10 Teacher: Konstantinos Estimation and classification using MRFs
May 8 Wednesday
10:00-11:45
Euler L11

Guest lecture: Mehdi Moradi (Umeå University)

Case study: Konstantinos.

 

 
  Wednesday
13:15-15:00
MVF24 and MVF25 E11  
 
May 13 Monday
8:30-9:45
Euler L12

Guest lecture: Nathan Gillot 

 
  Monday
13:15-15:00
MVF24 and MVF25 E12  
DEADLINE FOR PROJECT PART 1, 2 & 3
May 15 Wednesday 10:00-11:45 Euler
Wednesday
13:15-15:00
MVF24 and MVF25
May 20 Monday
8:00-9:45
Euler Project seminars
Monday
13:15-15:00
MVF24 and MVF25 Project seminars
May 22 Wednesday
10:00-11:45
Euler Project seminars
Wednesday
13:15-15:00
MVF24 and MVF25 Project seminars  
 

 

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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).

 

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Course summary:

Date Details Due