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

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

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).

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

Lectures and mini-projects in groups. 

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.

None

• 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/

Face-to-Face with selected online elements.