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Curriculum

Modul CS5450 T

Module part: Machine Learning (MaschLerna)

Duration:


1 Semester
Turnus of offer:


each winter semester
Credit points:


4
Course of studies, specific field and terms:
  • Master Biophysics 2023 (module part), advanced curriculum, 1st semester
  • Master Computer Science 2019 (module part), Module part, Arbitrary semester
  • Master MES 2020 (module part), computer science / electrical engineering, Arbitrary semester
  • Master Entrepreneurship in Digital Technologies 2020 (module part), Module part, Arbitrary semester
  • Master Biophysics 2019 (module part), advanced curriculum, 1st semester
  • Master IT-Security 2019 (module part), Module part, 1st or 2nd semester
  • Master Entrepreneurship in Digital Technologies 2014 (module part), Module part, Arbitrary semester
  • Master MES 2014 (module part), computer science / electrical engineering, 1st or 2nd semester
  • Master Computer Science 2014 (module part), Module part, Arbitrary semester
Classes and lectures:
  • Machine Learning (exercise, 1 SWS)
  • Machine Learning (lecture, 2 SWS)
Workload:
  • 20 Hours exam preparation
  • 55 Hours private studies
  • 45 Hours in-classroom work
Contents of teaching:
  • Representation learning, including manifold learning
  • Statistical learning theory
  • VC dimension and support vector machines
  • Boosting
  • Deep learning
  • Limits of induction and importance of data ponderation
Qualification-goals/Competencies:
  • Students can understand and explain various machine-learning problems.
  • They can explain and apply different machine learning methods and algorithms.
  • They can chose and then evaluate an appropriate method for a particular learning problem.
  • They can understand and explain the limits of automatic data analysis.
Grading through:
  • exam type depends on main module
Responsible for this module:
  • Siehe Hauptmodul
Teachers:
Literature:
  • Chris Bishop: Pattern Recognition and Machine Learning - Springer ISBN 0-387-31073-8
  • Vladimir Vapnik: Statistical Learning Theory - Wiley-Interscience, ISBN 0471030031
  • Tom Mitchell: Machine Learning - McGraw Hill. ISBN 0-07-042807-7
Language:
  • English, except in case of only German-speaking participants
Notes:

Admission requirements for taking the module:
- None

Admission requirements for participation in module examination(s):
- Successful completion of exercise assignments as specified at the beginning of the semester.

Module Exam(s):
- CS5450-L1: Machine Learning, oral exam, 100% of module grade.

(Is part of the module CS4290, CS4511, CS5400, CS4251-KP08)

Letzte Änderung:
13.9.2021