Data Analytics and Neural Networks (ILV)
Back| Course number | M2.03100.20.020 |
| Course code | DANN |
| Curriculum | 2024 |
| Semester of degree program | Semester 2 |
| Mode of delivery | Presencecourse |
| Units per week | 3,0 |
| ECTS credits | 5,0 |
| Language of instruction | English |
- Students are able to demonstrate ML and basic DL techniques to apply AI models to real world tasks and problems in the medical context.
- Students are able to:
- Identify ML models
- Identify DL models
- Identify data requirements
- Explicate problems and tasks
- Preprocess data
- Train and evaluate ML techniques
- Present results
Completion of the modules "Statistics", "Introduction to Machine Learning"
The area of data analytics and neural networks is a very dynamic area, and this description is exemplary, to be applied as an example and includes the state of knowledge of 2022:
- Data requirements
- Application of supervised and unsupervised techniques to data
- Neural Network fundamentals
- Data storytelling
- Lecture script as provided in the module (required)
- S. Papp et al: The Handbook of Data Science and AI, 2022
- F. Chollet: Deep Learning with Python. Manning, 2nd ed. 2020
- P. Johannesson & E. Perjons: An Introduction to Design Science, 2014
teaching & discussion, demonstration, practical examples, groupwork in teams, homework
integrated module examination
immanent examination character: presentation, assignment reports, exam (oral/written)
