Module CS5450-KP04, CS5450
Machine Learning (MaschLern)
Duration
1 Semester
Turnus of offer
each winter semester
Credit points
4
Course of studies, specific fields and terms:
- Master CLS 2023, optional subject, computer science
- Master Auditory Technology 2022, optional subject, computer science
- Master MES 2020, optional subject, computer science / electrical engineering
- Master Media Informatics 2020, optional subject, computer science
- Master Medical Informatics 2019, optional subject, Medical Data Science / Artificial Intelligence
- Master Auditory Technology 2017, optional subject, computer science
- Master CLS 2016, optional subject, computer science
- Master MES 2014, optional subject, computer science / electrical engineering
- Master MES 2011, optional subject, mathematics
- Master MES 2011, advanced curriculum, imaging systems, signal and image processing
- Master Medical Informatics 2014, optional subject, computer science
- Master CLS 2010, optional suject, computer science
- Master Computer Science 2012, optional subject, specialization field robotics and automation
- Master Computer Science 2012, optional subject, specialization field bioinformatics
Classes and lectures:
- Machine Learning (exercise, 1 SWS)
- Machine Learning (lecture, 2 SWS)
Workload:
- 45 hours in-classroom work
- 55 hours private studies
- 20 hours exam preparation
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:
- Oral examination
Responsible for this module:
Literature:
- Chris Bishop : Pattern Recognition and Machine Learning Springer ISBN 0-387-31073-8
- Vladimir Vapnik : Statistical Learning Theory Wiley-Interscience, ISBN 0471030031
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):
- None
Module exam(s):
- CS5450-L1: Machine Learning, oral examination, 100% of module grade
Last Updated:
02.02.2022