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.

Common Computer Vision Tasks

General Information


Per-Erik Forssén Michael Felsberg Bastian Wandt
Lectures, Examiner Lectures Lectures
Ziliang Xiong Johan Edstedt Pavlo Melnyk Cuong Le Emanuel Sanchez Aimar
Supervisor Supervisor Supervisor Supervisor 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.

Lecture Schedule HT2023

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
August 28: 08.15-10
Lecture 1
Per-Erik Forssén
August 29: 10.15-12
Lecture 2
Feature Descriptors
Per-Erik Forssén
August 30: 13.15-15
Lecture 3
Convolutional Neural Networks: Introduction and Theory
Michael Felsberg
September 1: 8.15-10.00
Lecture 4
Image Classification with Convolutional Neural Networks
Michael Felsberg
September 5: 10.15-12
Lecture 5
Project 1: Image Classification
Per-Erik Forssén
September 6: 13.15-15
Lecture 6
Compound Descriptors, Metric Learning, and Evaluation
Per-Erik Forssén
September 8: 8.15-10
Lecture 7
Visual Object Detection
Per-Erik Forssén
September 20: 13.15-15
S23, S25
Seminar 1
Presentation of project 1
Per-Erik Forssén
Bastian Wandt
September 20: 15.15-17
Lecture 8
Semantic and Panoptic Segmentation using CNNs
Bastian Wandt
September 22: 8.15-10
Lecture 9
Project 2: Semantic Segmentation
Per-Erik Forssén
September 26: 10.15-12
Lecture 10
Visual Object Tracking with Deep Features
Bastian Wandt
October 11: 13.15-15
Seminar 2
Presentation of project 2
Per-Erik Forssén
Bastian Wandt
October 11: 15.15-17
Seminar 2
Discussion of project 2 and exam
Per-Erik Forssén
Bastian Wandt

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 Olympen and Asgård. TimeEdit also lists backup lectures. Note that teachers will only be present at activities listed in the table above.


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

Project 2: Semantic Segmentation

General resources

We recommend using the following software:

Other related software of interest:

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.