Data Analytics and Neural Networks (ILV)

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Course lecturer:

FH-Prof. Dipl.-Math. Dr. phil. habil.

 Andreas Pester
Course numberM2.03100.20.020
Course codeDANN
Curriculum2024
Semester of degree program Semester 2
Mode of delivery Presencecourse
Units per week3,0
ECTS credits5,0
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
Necessary theoretical concepts are introduced and explained on the basis of practical projects.

  • 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)