Studienhandbuch | System Monitoring and Data Science

Inhalt

[ 479SMDS24 ] Subject System Monitoring and Data Science

Workload	Mode of examination	Education level	Study areas	Responsible person	Coordinating university
9 ECTS	Accumulative subject examination	M2 - Master's programme 2. year	^(*)Kunststofftechnik	Gerald Roman Berger-Weber	Johannes Kepler University Linz

Detailed information

Original study plan

Master's programme Polymer Engineering and Science (PES) 2025W

Learning Outcomes

Competences
These result from the subordinate elements

Skills	Knowledge
These result from the subordinate elements	These result from the subordinate elements

Further information

Students are able to solve current problems from Polymer Engineering and Science by applying knowledge and skills from System Monitoring and Data Science.

Subordinated subjects, modules and lectures
UE Digital Signal Processing
VL Digital Signal Processing
UE Machine Learning: Supervised Techniques
VL Machine Learning: Supervised Techniques
UE Machine Learning: Unsupervised Techniques
VL Machine Learning: Unsupervised Techniques
VL Statistical Methods
UE Artificial Intelligence
VL Artificial Intelligence
UE Embedded and Pervasive Systems
VL Embedded and Pervasive Systems
VL Introduction to Machine Learning
VL Software Engineering
KV Computational Data Analytics
PR Computational Data Analytics
UE Computer Vision
VL Computer Vision
UE Visualization
VL Visualization
KV Applied Measurement and Control in Polymer Processing
VL Structural Health Monitoring
UE Statistical Methods