Studium

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Objectives

Introduction to selected mathematical methods in machine learning and their application to physical processes. The seminar will provide training in the following skills:
  • Literature research
  • Planning and execution of a small research project
  • Presentation and discussion of the results
  • Publication of the results if adequate

Content

Students will be given small research projects related to machine learning. The project will include learning a selected mathematical method or idea from the area of machine learning, the implementation of this idea (usually in Python), and its application to data from the areas of physical simulation or signal processing. Examples for possible topics:

  • Clustering
  • Dimension reduction
  • Sparse regression
  • Cross-Validation
  • Dynamic Mode Decomposition / Blind Source Separation
  • Sparse Matrix approximation

In addition to working on the project, we will regularly meet, present and discuss. The lecturer will give introductions to project areas and will try to connect these areas and ideas to a broader understanding of machine learning.

Requirements

To successfully complete the project, students need to be proficient and confident in linear algebra and programming (ideally with Python). They must be able to work independently as they will have to assess and understand mathematical and computational problems new to them, search for already existing solutions on their own, and implement/apply them.

Objectives

Introduction to selected mathematical methods in machine learning and their application to physical processes. The seminar will provide training in the following skills:
  • Literature research
  • Planning and execution of a small research project
  • Presentation and discussion of the results
  • Publication of the results if adequate

Content

Students will be given small research projects related to machine learning. The project will include learning a selected mathematical method or idea from the area of machine learning, the implementation of this idea (usually in Python), and its application to data from the areas of physical simulation or signal processing. Examples for possible topics:

  • Clustering
  • Dimension reduction
  • Sparse regression
  • Cross-Validation
  • Dynamic Mode Decomposition / Blind Source Separation
  • Sparse Matrix approximation

In addition to working on the project, we will regularly meet, present and discuss. The lecturer will give introductions to project areas and will try to connect these areas and ideas to a broader understanding of machine learning.

Requirements

To successfully complete the project, students need to be proficient and confident in linear algebra and programming (ideally with Python). They must be able to work independently as they will have to assess and understand mathematical and computational problems new to them, search for already existing solutions on their own, and implement/apply them.

Sprachübergreifend

Werdende Mütter

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Stillende Mütter

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Mutterschutzbogen anzeigen