TSBB19 Machine Learning for Computer Vision
This course gives a theoretical and practical introduction to machine learning tasks in computer vision. Theory is introduced in a series of lectures that present and illustrate current methods for object recognition, detection and tracking. The course also has two small projects that are solved in groups. Projects are presented both in written form and orally, at two seminars. The course ends with a written exam on the theoretical content.
General Information
- Course syllabus
Course syllabus can be found in the Study guide.
- Registration
The 2025 course starts on September 1. To receive emails about the course and to have your results entered into Ladok you must be registered on the course. If you intend to take the course but have not yet registered, make sure to do it promptly through Ladok, or via the Student Portal Lisam.
- Schedule
The course schedule can be found in TimeEdit.
- Examination
See the examination page.
People
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 |
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| Per-Erik Forssén Lectures, Examiner |
Urs Waldmann Lectures |
Michael Felsberg Lectures |
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|
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| Ziliang Xiong Supervisor |
James Waguespack Supervisor |
Cuong Le Supervisor |
Ioannis Athanasiadis Supervisor |
Urs Waldmann Supervisor |
Anmar Karmush Supervisor |
Gulnaz Zhambulova Supervisor |
Course material
The main course material consists of lecture slides, and selected articles. We also recommend the two books listed below for a more in-depth treatment of the content.
- Course GIT repositories
The course has three GIT repositories. These are hosted in LiUs GitLab (accessible to anyone with a LiU login). One repository contains the course material (e.g. lecture slides, and articles for background reading), and the other two contain support code for the projects.
- Book 1:
Goodfellow et al., Deep Learning, MIT Press (2016).
Relevant contents: Theory on machine learning, convolutional neural networks, loss functions, and regularization.
See book webpage for chapter PDFs Book webpage. - Book 2:
Richard Szeliski, Computer Vision: Algorithms and Applications, 2nd ed., Springer Verlag (2022).
Relevant contents: classical computer vision, feature extraction, optimization.
The book is available as an on campus e-book via the LiU library. See also Book webpage.
Lecture Schedule HT2025
Before the lectures, the lecture slides from last year can be found in the course material repository. Updated slides will be pushed after the lecture has taken place. The repository also contains additional relevant literature for each lecture.
| Date,Time,Room | Activity | Teacher |
|---|---|---|
|
September 1: 08.15-10 A38 |
Lecture 1 Introduction |
Per-Erik Forssén |
|
September 2: 10.15-12 A31 |
Lecture 2 Feature Descriptors |
Per-Erik Forssén |
|
September 3: 13.15-15 U2 |
Lecture 3 Convolutional Neural Networks: Introduction and Theory |
Urs Waldmann |
|
September 5: 8.15-10.00 A31 |
Lecture 4 Image Classification with Convolutional Neural Networks |
Urs Waldmann |
|
September 9: 10.15-12 C3 |
Lecture 5 Project 1: Image Classification |
Per-Erik Forssén |
|
September 10: 13.15-15 FB113 |
Lecture 6 Compound Descriptors, Metric Learning, and Evaluation |
Per-Erik Forssén |
|
September 12: 8.15-10 A32 |
Lecture 7 Visual Object Detection |
Per-Erik Forssén |
|
September 24: 13.15-15 A33, A34 |
Seminar 1 Presentation of project 1 |
Per-Erik Forssén Urs Waldmann |
|
September 24: 15.15-17 A31 |
Lecture 8 Semantic and Panoptic Segmentation |
Urs Waldmann |
|
September 26: 8.15-10 A36 |
Lecture 9 Project 2: Semantic Segmentation |
Per-Erik Forssén |
|
October 6: 8.15-10 S26 |
Lecture 10 Visual Object Tracking with Deep Features |
Michael Felsberg |
|
October 15: 13.15-15 KY31, KY34 |
Seminar 2 Presentation of project 2 |
Urs Waldmann Per-Erik Forssén |
|
October 15: 15.15-17 KY34 |
Seminar 2 Discussion of project 2 and exam |
Per-Erik Forssén Urs Waldmann |
A more extensive schedule can be found in TimeEdit. It also contains scheduled project time (labelled "Projekttid"). These are times when you have exclusive access to the computer rooms Egypten, Asgård and Olympen. TimeEdit also lists backup lectures. Note that teachers will only be present at activities listed in the table above.
Projects
The projects are conducted in groups of 5 or 4 students (in order of preference).
Groups and supervisor assignments are finalized at the introductory
lecture of project 1, and will be published in the course
repository.
Project 1: Visual Object Recognition
- Important dates:
Introductory lecture September 9 Report to supervisor September 17 Report due September 21 (a Sunday) Presentation seminar September 24 - Course material for project 1
- GIT for project 1 utility code
Project 2: Semantic Segmentation
- Important dates:
Introductory lecture September 26 Report to supervisor October 8 Report due October 12 (a Sunday) Presentation seminar October 15 - Course material for project 2
- GIT for project 2 utility code
Compute resources
Run your jobs on the computers in Egypen, Asgård and Olympen.
- TimeEdit for TSBB19 (to see when you have exclusive access)
- TimeEdit for Egypten (to find spare time slots)
- TimeEdit for Asgård (to find spare time slots)
- TimeEdit for Olympen (to find spare time slots)
- RDP client status (to see which computers are in use, switched off etc)
General resources
We recommend using the following software:
- PyTorch A deep learning framework for Python (support code for the projects is written for PyTorch).
- PyCharm A Python IDE (installed in the Linux labs).
Other related software of interest:
- OpenCV 4.8 (Open Source Computer Vision) library that is installed at ISY labs. Python bidnings exist.
- LIBSVM A Library for Support Vector Machines (Matlab, Python).
- VLFeat has a a useful code library, both for Matlab and C/C++.
Project Repositories
Project code should be developed under versioning control, with changes tracked according to LiU-ID of the participating group members.
Project groups should get their repositories from GITLab at LiU.
Note: this is not GitHub, and GitHub should not be used.