Studienhandbuch | Computer and Data Science

Inhalt

^(*) Leider ist diese Information in Deutsch nicht verfügbar.

Workload	Form der Prüfung	Ausbildungslevel	Studienfachbereich	VerantwortlicheR	Anbietende Uni
	Gliederung	M1 - Master 1. Jahr	Informatik	Sepp Hochreiter	Johannes Kepler Universität Linz

Detailinformationen
Quellcurriculum	Masterstudium Artificial Intelligence 2019W
Ziele	^(*)Specialization topics in computer and data science.
Lehrinhalte	^(*)The contents of this subject result from the contents of its courses.

Untergeordnete Studienfächer, Module und Lehrveranstaltungen
KV ^(*)Basic Methods of Data Analysis
UE ^(*)Computational Logics for AI
VL ^(*)Computational Logics for AI
UE ^(*)Machine Learning: Supervised Techniques
VL ^(*)Machine Learning: Supervised Techniques
UE ^(*)Machine Learning: Unsupervised Techniques
VL ^(*)Machine Learning: Unsupervised Techniques
KV ^(*)Natural Language Processing
UE ^(*)Programming in Python II
VL ^(*)Programming in Python II
UE ^(*)Reinforcement Learning
VL ^(*)Reinforcement Learning
UE ^(*)Statistics for AI
VL ^(*)Statistics for AI
UE ^(*)Visual Analytics
UE ^(*)Symbolic AI
VL ^(*)Symbolic AI
UE Softwareentwicklung 2
VL Softwareentwicklung 2
KV Big Data Management and Processing
KV Computational Data Analytics
KV Conceptual Data Modeling
KV Debugging
KV Emerging Computer Technologies
KV Hardware Design
VL Information Displays
KV Information Visualization
KV Knowledge Based Systems
KV Learning from User-generated Data
UE Machine Learning and Pattern Classification
VL Machine Learning and Pattern Classification
KV Mobile Computing
KV Model Checking
KV Multimedia Search and Retrieval
UE Principles of Cooperation
VL Principles of Cooperation
UE Principles of Interaction
VL Principles of Interaction
KV SAT Solving
KV Semantic Data Modeling and Applications
VL Visual Analytics
KV ^(*)Statistical Principles of Data Science
KV ^(*)Computational Statistics