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 2021W
Objectives	By selecting courses from this category, students can deepen and broaden their knowledge and skills in computer science set a specific focus in their studies
Subject	The contents of this subject result from the contents of the selected courses.
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 Deep Learning and Neural Nets I
VL Deep Learning and Neural Nets I
UE Explainable AI
VL Explainable AI
UE Planning and Reasoning in Artificial Intelligence
VL Planning and Reasoning in Artificial Intelligence
UE Theoretical Concepts of Machine Learning
VL Theoretical Concepts of Machine Learning
UE Sequence Analysis and Phylogenetics
VL Sequence Analysis and Phylogenetics
VL Computational Geometry
KV Advanced Compiler Construction
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 Information Visualization
KV Introduction to Linux
KV Mobile Computing
KV Mobile Web Development
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
VL Quantum Computing
KV SAT Solving
KV Semantic Data Modeling and Applications
KV Statistics 2
UE Visual Analytics
KV Web Engineering
KV Web Performance
KV Web Search and Mining
KV Web Security
KV Web Usability
KV Advanced Cryptology
KV VLSI Design
UE Computational Geometry
UE Computer Algebra
VL Computer Algebra
VL Formal Semantics of Programming Languages
VL Rewriting in Computer Science and Logic
KV Advanced Regression Analysis
KS Social and gender IT aspects
VL Ethics and Gender Studies
KV Gender Studies Managing Equality TN
KV Multivariate Statistics
KV Generalized linear models