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Mathematical Foundations of Machine Learning

CIT413048Elective Modules9 ECTSEnglishsummer semesterDepartment Mathematics
AI-edited module sheet. Based on the TUMonline module description, edited for readability.Original in TUMonline

What it is about

You will learn the mathematical foundations of modern methods of machine learning. By the end you can design neural networks and discuss their approximation properties, apply kernel methods in Reproducing Kernel Hilbert Spaces, and assess the statistical efficiency of learning procedures.

What you will be able to do

  • Understand basic terms and concepts of machine learning
  • Design and implement neural networks
  • Discuss the approximation properties of neural networks
  • Understand and apply the theory of kernel methods in RKHS
  • Determine nonlinear data regressions with kernel methods
  • Assess the statistical efficiency of ML methods

What the module consists of

  • VorlesungDelivering the theoretical content through lectures and presentations
  • ÜbungSolving examples and group work to deepen understanding

Teaching method

  • Vortrag/Präsentationfor introduction and presentation of the content
  • Selbststudium der Literaturfor deepening and independent engagement
  • Übungen in Gruppenarbeitfor applying to concrete questions and examples
  • Moodlefor providing materials
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Official page in TUMonline · Details are not binding.