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Artificial Intelligence (MGST-B-5-MBAI-AI-ILV)
Department
- Bachelor's program Medical, Health and Sports Engineering
Course unit code
- MGST-B-5-MBAI-AI-ILV
Level of course unit
- Bachelor
Year of study
- Fall 2024
Semester when the course unit is delivered
- 5
Number of ECTS credits allocated
- 2.0
Name of lecturer(s)
- FH-Prof. Hollaus Bernhard, PhD
Learning outcomes of the course unit
- Since its beginnings, the field of artificial intelligence has always been used in the medical field. While it used to be possible to capture expert knowledge in explicit rules and thus support medical diagnosis, for example, this is no longer possible today. On the one hand, because the complexity of the data has steadily increased, on the other hand, because in many cases (e.g. in the analysis of radio-logical images) no explicit rules can be formulated. In particular, the field of machine learning, as a sub-field of artificial intelligence, has emerged in recent years as an extremely useful tool for the analysis of such data. The students should gain access to different methods of machine learning and be able to use these methods to solve practical problems with the help of current software libraries.
Recommended optional program components
- keine
Course contents
- - Overview of AI and ML in a historical context
- Linear regression as the simplest form of ML
- Decision trees and SVMs for data classification
- Basics of neural networks
- Basics of Convolutional Neural Networks (CNNs)
- CNNs for classification of radiological and dermatological images
- CNNs for segmentation of radiological images
- Data Augmentation
- LSTMs for time series analysis of medical data
- Machine Learning in Natural Language Processing for analysis and processing of textual data.
- Interpretability of ML models and ethical problems of AI
- Accompanying the topics with a practice-relevant problem using industry-relevant software.
Recommended or required reading
- - S. L. Brunton and J. N. Kutz. Data-Driven Science and Engineer-ing: Machine Learning, Dynamical Systems, and Control. Cam-bridge 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
Planned learning activities and teaching methods
- In der Lehrveranstaltung werden verschiedenste Lehr- und Lernformen (Vortrag, Einzel- und Gruppenarbeit, Diskussion, etc.) auf interaktive Art und Weise verknüpft.
Language of instruction
- English
Work placement(s)
- keine