Studienhandbuch | General Electives

Inhalt

[ 921CGEL13 ] Subject General Electives

Workload	Mode of examination	Education level	Study areas	Responsible person	Coordinating university
0-27 ECTS	Structure	M - Master's programme	Computer Science	Hanspeter Mössenböck	Johannes Kepler University Linz

Detailed information

Original study plan

Master's programme Computer Science 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

The General Electives are regularly offered courses (at least every 2 years) that are explicitly described in the study handbook.

Subordinated subjects, modules and lectures
UE Machine Learning: Unsupervised Techniques
VL Machine Learning: Unsupervised Techniques
UE Natural Language Processing
VL Natural Language Processing
UE Reinforcement Learning
VL Reinforcement Learning
UE AI and Visualization
VL AI and Visualization
UE Biological Sequence Analysis
VL Biological Sequence Analysis
UE Deep Learning: Basic Techniques
VL Deep Learning: Basic Techniques
UE Machine Learning: Advanced Techniques
VL Machine Learning: Advanced Techniques
UE Planning and Reasoning in Artificial Intelligence
VL Planning and Reasoning in Artificial Intelligence
VL Computational Geometry
KV Advanced Compiler Construction
KV Advanced Interactive Visualization
KV Advanced Operating Systems
KV Application Oriented Knowledge Processing
KV Assistive Technologies and Accessibility
KV Big Data Engineering
VL Biometric Identification
KV Cloud Computing
KV Cloud Security
PR Computational Data Analytics
UE Computer Algebra for Concrete Mathematics
UE Computer Forensics and IT Law
KV Conceptual Data Modeling
KV Debugging
KV Digital Image Processing
KV Engineering of AI-intensive Systems
KV Human/Computer Interaction
VL Information Displays
KV Introduction to Linux
KV Mobile Computing
KV Mobile Web Development with Python and JavaScript
KV Model Engineering for Data-Intensive Systems
KV Modeling and Computer Simulation
KV Modeling Internet Applications
KV Practical Introduction to Modern System Design with C++
UE Probabilistic Models
KV Product Line Engineering
UE Quantum Computing
VL Quantum Computing
KV SAT Solving
KV Semantic Data Modeling and Applications
KV Statistics 2
UE Visualization
KV Web Engineering
KV Web Performance
KV Web Search and Mining
KV Web Security
KV Advanced Cryptology
KV VLSI Design
UE Computational Geometry
UE Computer Algebra
VL Computer Algebra
KV Advanced Regression Analysis
KS Social and gender IT aspects
KV Ethics and Gender Studies
KV Gender Studies Managing Equality TN
KV Multivariate Statistics
KV Generalized linear models