Information on individual educational components (ECTS-Course descriptions) per semester

Computer Vision

Degree programme Computer Science
Subject area Engineering Technology
Type of degree Master
Full-time
Summer Semester 2025
Course unit title Computer Vision
Course unit code 024913120505
Language of instruction English
Type of course unit (compulsory, optional) Elective
Teaching hours per week 2
Year of study 2025
Level of the course / module according to the curriculum
Number of ECTS credits allocated 4
Name of lecturer(s) Sebastian HEGENBART
Requirements and Prerequisites

Basics in machine learning, statistics, probability theory and linear algebra. 

Course content

Computer Vision deals with the automated analysis and interpretation of visual data. This scientific field has gained a lot of traction due to the impressive success of deep-learning within the last few years and builds the basis for a plethora of exciting modern applications. In this course the principles of Computer Vision are covered:

  • basics in image analysis (edge detection, filtering, smoothing)
  • deep-learning in computer vision: convolutional neural networks, fully convolutional networks, generative adversarial networks, variational autoencoders
  • training and evaluation of deep-learning based models
  • methods in image classification
  • methods in object detection
  • methods in image segmentation
  • generative approaches

Examples and projects will be developed in python using frameworks such as scikit-learn, openCV, numpy and keras (tensorflow).

Learning outcomes

The students are able to:

  • use classical methods for image analysis to solve problems
  • select the appropriate method for a given problem
  • explain the differences between deep-learning based approaches and classical neural networks
  • create, train and evaluate deep-learning based models
  • read and understand publications in the field
  • employ and adapt state-of-the-art architectures using keras and tensorflow
Planned learning activities and teaching methods

Lectures and mini-projects in groups. 

Assessment methods and criteria

Written exam 75%

Exercise 25%

For a positive grade, a minimum of 50% of the possible points must be achieved in each part of the examination.

Comment

None

 

Recommended or required reading
  • Goodfellow, Ian; Bengio, Yoshua; Courville, Aaron (2016): Deep Learning. MIT Press. Available at: URL: https://www.deeplearningbook.org/
  • Norvig, Peter; Russel, Stuart (2021): Artificial Intelligence: A Modern Approach, Global Edition.
  • Szeliski, Richard (2021): Computer Vision: Algorithms and Applications. Available at: URL: https://szeliski.org/Book/

 

Mode of delivery (face-to-face, distance learning)

Face-to-Face with selected online elements.