Artificial Intelligence

Department

Bachelor's program Medical, Health and Sports Engineering

Course unit code

MGST-B-5-MAI-AI-ILV

Number of ECTS credits allocated

2.0

Name of lecturer(s)

Mode of delivery

face-to-face

Recommended optional program components

none

Recommended or required reading

- T. J. R. Hughes. The finite element method: linear static and dynamicfinite element analysis, volume 682. Dover Publications New York, 2000.
- A. Meyer-Baese and V. Schmid. Pattern Recognition and Signal Analysis in Medical Imaging, 2nd Edition. Elsevier Academic Press, 2014.
- S. L. Brunton and J. N. Kutz. Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control. Cambridge University Press, 2019.
- C. M. Bishop. Pattern Recognition and Machine Learning. Springer, 2007
- I. Goodfellow, Y. Bengio, and A. Courville. Deep Learning. The MIT press, 2016

Level of course unit

Bachelor

Year of study

Fall 2025

Semester when the course unit is delivered

5

Language of instruction

English

Learning outcomes of the course unit

understand the fundamentals of machine learning and its applications.
are able to solve practical problems in the field of medical technology using machine learning methods.
know the theory behind a support vector machine and can reproduce it.
are aware of the limitations of what they have learned.

Course contents

The field of artificial intelligence has been used in the medical domain since its inception. In the past, it was possible to capture expert knowledge in explicit rules and use them, for example, to support medical diagnoses. However, this is no longer feasible today. On the one hand, the complexity of data has continuously increased; on the other hand, in many cases (e.g., in the analysis of radiological images), explicit rules cannot be formulated. In recent years, machine learning, as a subfield of artificial intelligence, has proven to be an extremely useful tool for analyzing such data.

Students should gain access to various machine learning methods and be able to apply these methods using modern software libraries to solve practical problems.

Course Topics:

Overview of AI and ML in a historical context
Linear regression as the simplest form of ML
Decision trees and SVMs for data classification
Fundamentals of neural networks
Basics of convolutional neural networks (CNNs)
CNNs for classifying radiological and dermatological images
CNNs for segmenting radiological images
Data augmentation
LSTMs for time series analysis of medical data
Machine learning in the field of natural language processing for analyzing and processing textual data
Interpretability of ML models and ethical issues in AI
Practical case studies using industry-relevant software for each topic

Planned learning activities and teaching methods

The course comprises an interactive mix of lectures, discussions and individual and group work.

Work placement(s)

none