TDP033 · 6 credits · Basic level (G2F) · Programmering
Data-centric Artificial Intelligence
Active · next start HT 2026
No exam statistics yet
There is no exam sitting with at least 5 students in the last three academic years. New courses and project-only courses often have no statistics.
Part of 1 programme
About the course
Aim
After completing the course, the student should be able to:
- explain and apply basic concepts in data-centric artificial intelligence
- explain and systematically reflect on the applicability of different methods in data-centric artificial intelligence for different types of learning problems
- use existing tools to construct, train, and analyze deep neural network models
- develop and adapt code to preprocess, analyze, and improve the quality of data sets
Content
The course provides basic methodological knowledge in data-centric artificial intelligence through the following content:
- Basic concepts in artificial neural networks for deep learning
- Methods and tools for construction and adaptation, training, and evaluation of models (Python, Keras, Tensorflow, Colab)
- Data-centric methods for detailed error analysis of models and underlying data
- Structured approach to managing and correcting (curating) data
- Examiner
- Rita Kovordanyi
- Scheduled hours
- 0 h
- Main field of study
- Programmering