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)
  • Nocker Martin, MSc
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
    - M. Nielsen, Neural Networks and Deep Learning. Determination Press, 2015. online: http://neuralnetworksanddeeplearning.com/
    - A. Géron, Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 2nd Edition. 2019.
Additional information about examination modalities
  • Course with final exam, and practical assignment(s).
Level of course unit
  • Bachelor
Year of study
  • Fall 2026
Semester when the course unit is delivered
  • 5
Language of instruction
  • English
Learning outcomes of the course unit
  • Students ...
    - understand the fundamentals of machine learning and its applications.
    - know the difference between supervised and unsupervised learning.
    - know and are able to implement regression, classification and clustering.
    - are able to solve practical problems in the field of medical technology using machine learning methods.
    - know the most important factors that determine the performance of machine learning models - and can apply it in real problems.
    - 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 methods in AI and ML in particular
    - Linear regression as the simplest form of Supervised Learning algorithm
    - Regression vs. Classification
    - Insights into Support Vector Machines and Decision Trees
    - Basics of Neural Networks: Fully-connected Networks, Convolutional Neural Networks (CNNs)
    - Clustering methods as important Unsupervised Learning algorithms
    - Introduction of Semi-Supervised Learning
    - Accompanying the topics with a practice-relevant problem using industry-relevant software.

    Practical Project
    - Training and deploying a state-of-the-art object detection model
    - Dataset generation

    After this lecture students are ...
    - able to confidently use appropriate terms and language when discussing ML
    - know fundamental concepts of AI and ML
    - able to choose and implement the right ML algorithms for different real-world problems
    - know the limitations of different ML algorithms
    - confident to implement ML pipelines using Python
Planned learning activities and teaching methods
  • The course comprises an interactive mix of lectures, discussions and individual and group work.
Work placement(s)
  • none

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