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Module Guide before WS 2016/17

Modul MA5036-KP05

Multi- and High-Dimensional Data Processing (MeHoDVKP05)

Duration:


1 Semester
Turnus of offer:


irregularly
Credit points:


5
Course of studies, specific field and terms:
  • Master CLS 2016 (optional subject), mathematics, 2nd semester
  • Bachelor CLS 2016 (optional subject), mathematics, 6th semester
Classes and lectures:
  • Multi- and High-Dimensional Data Processing (exercise, 1 SWS)
  • Multi- and High-Dimensional Data Processing (lecture, 2 SWS)
Workload:
  • 65 Hours private studies and exercises
  • 45 Hours in-classroom work
  • 10 Hours exam preparation
  • 30 Hours work on project
Contents of teaching:
  • Energy-based methods for data processing
  • Data terms and regularizers for non-scalar data
  • Basics of differential geometry
  • Manifold-constrained optimization
  • Linear, non-linear, and robust dimensionality reduction
  • Applications in statistics, image-/video processing, machine learning, and com- puter vision
Qualification-goals/Competencies:
  • The students understand the difficulties when moving from scalar to higher-dimensional data.
  • They are confident in selecting and implementing a suitable model for a given problem from a set of known models.
  • They understand the special issues when solving manifold-constrained problems.
  • They are familiar with selected methods for manifold-constrained optimization and are confident in their implementation.
  • They are familiar with selected methods for linear and non-linear dimensionality reduction.
  • Interdisciplinary qualifications:
  • Students have advanced skills in modeling.
  • They can translate theoretical concepts into practical solutions.
  • They are experienced in implementation.
  • They can think abstractly about practical problems.
Grading through:
  • Oral examination
Requires:
Responsible for this module:
Teachers:
Literature:
  • Absil: Optimization Algorithms on Matrix Manifolds - Princeton University Press
Language:
  • German and English skills required
Notes:

Prerequisite tasks for taking the exam can be announced at the beginning of the semester. If any prerequisite tasks are defined, they must be completed and passed before taking the exam for the first time.

Letzte Änderung:
13.8.2019