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Introduction to Regularization and Learning Methods for Inverse Problems

CIT413070Elective Modules5 ECTSEnglishUnregelmäßigDepartment 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 inverse problems and regularization as well as modern data-driven solution approaches. In the end you will be able to analyze inverse problems, apply classical regularization methods and classify data-based reconstruction methods.

What you will be able to do

  • Mathematical formulation of typical challenges in inversion problems
  • Understanding of pseudoinversion and its instabilities in infinite dimensions
  • Knowledge of theoretical and practical regularization strategies (SVD, Tikhonov, Sparsity, TV)
  • Application of convex optimization methods to solve variational problems
  • Foundations of modern machine-learning methods and setup/training of neural networks
  • Application and evaluation of data-driven approaches for inverse problems and awareness of their risks

What the module consists of

  • VorlesungDelivery of theoretical content with examples and discussion
  • Übung/Practice SessionsWorking on exercise sheets and deepening the lecture material

Teaching method

  • Vortrag mit DemonstrationenPresentation of content and motivation for independent analysis
  • Diskussion mit StudierendenInvolvement to clarify and deepen topics
  • Praktische ÜbungssitzungenIndependent work on tasks with solutions for deepening understanding
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Official page in TUMonline · Details are not binding.