Studienhandbuch | System Monitoring and Data Science

Inhalt

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) 2024W
Objectives	Students are able to solve current problems from Polymer Engineering and Science by applying knowledge and skills from System Monitoring and Data Science.
Subject	The content of teaching results from the content of the individual courses

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