Turnus of offer:
each winter semester
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)
- 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
- Deep learning
- Limits of induction and importance of data ponderation
- 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.
- exam type depends on main module
Responsible for this module:
- 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
- English, except in case of only German-speaking participants
Admission requirements for taking the module:
Admission requirements for participation in module examination(s):
- Successful completion of exercise assignments as specified at the beginning of the semester.
- 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