Master’s programmes at the Centre for Artificial Intelligence
The Centre for Artificial Intelligence offers Master’s programmes in the Computer Science and Data Science profiles. The programmes focus on machine learning and deep learning.
In the Data Science profile at the CAI, you will acquire advanced knowledge in various fields within machine learning, from the fundamentals to neural networks to reinforcement learning, and discover the related applications for modalities such as images (computer vision), text (natural language processing) or time series (e.g. acoustic data). Concurrently, you will conduct your own research in our research groups (Computer Vision, Perception and Cognition, Natural Language Processing), learn scientific writing and generate practical benefit side by side with our industry partners.
In the Computer Science profile at the CAI, you will have the opportunity to combine a specialisation in deep learning and its areas of application with an in-depth exploration of Computer Science. You may find this particularly appealing if you wish to expand the solid programming skills you have previously acquired in an engineering degree programmes by adding both AI-related and formal computer science skills. Concurrently, you will conduct your own research and generate practical benefit in the CAI’s research groups (Computer Vision, Perception and Cognition, Natural Language Processing).
You can find the key modules including the context modules (CMs), extended fundamental theoretical principles (FTPs) and technical scientific specialisation modules (TSMs) in the module browser (link). In addition, the centre offers the following decentralised specialisation modules (EVAs):
- EVA Artificial Intelligence Seminar (PDF 388,5 KB) (2 ECTS, spring semester): The goal of the AI Seminar is to learn the art of scholarship, specifically, to read and write original scientific work. To this end, each participant picks an AI research paper according to his or her research interests that serves as an example throughout the course.
- EVA Machine Intelligence Lab (PDF 217,7 KB) (4 ECTS, autumn semester): You will complete a public MOOC in the area of reinforcement learning, supervised by your ZHAW lecturers. After successful completion, you will be able to put your acquired skills to the test in a one-week hackathon, gaining valuable application know-how in a specialised area of machine learning.
One key success factor for an exceptional master’s project or thesis is the right research question, that is, the right question at the right time, combined with a good idea, might lead to unimagined progress in both research and practice. Our researchers keep lists of such good ideas that often lead to the publication of original papers and to industry solutions when combined with the skills of our master’s students. Sometimes, they even win awards.
Previous theses include
- Visual Two-Stage Manufacturing Quality Control using Deep Learning (with paper for the 2021 Swiss Data Science Conference)
- Real-World Speaker Recognition on VoxCeleb2 using Angular Margin Losses
- Learning to Cluster (with paper for the 2018 Artificial Neural Networks in Pattern Recognition Workshops, 2018 Dr Waldemar Jucker Award from the Society for the Advancement of Software Technology)
- Fully Convolutional Neural Networks for newspaper Article Segmentation (with paper for the «International Conference on Document Analysis and Recognition» 2017)