Studienhandbuch | Computer and Data Science

Inhalt

[ 993SCDS19 ] Subject Computer and Data Science

Workload	Mode of examination	Education level	Study areas	Responsible person	Coordinating university
	Structure	M1 - Master's programme 1. year	Computer Science	Sepp Hochreiter	Johannes Kepler University Linz

Detailed information
Original study plan	Master's programme Artificial Intelligence 2019W
Objectives	Specialization topics in computer and data science.
Subject	The contents of this subject result from the contents of its courses.

Subordinated subjects, modules and lectures
KV Basic Methods of Data Analysis
UE Computational Logics for AI
VL Computational Logics for AI
UE Introduction to Computational Statistics
VL Introduction to Computational Statistics
UE Machine Learning: Supervised Techniques
VL Machine Learning: Supervised Techniques
UE Machine Learning: Unsupervised Techniques
VL Machine Learning: Unsupervised Techniques
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 Knowledge Representation and Learning
VL Knowledge Representation and Learning
UE Planning and Reasoning in Artificial Intelligence
VL Planning and Reasoning in Artificial Intelligence
UE Computability and Complexity
VL Computability and Complexity
UE Software Development 2
VL Software Development 2
KV Big Data Management and Processing
KV Computational Data Analytics
KV Conceptual Data Modeling
KV Debugging
KV Engineering of AI-intensive Systems
KV Hardware Design
VL Information Displays
KV Information Visualization
KV Knowledge Based Systems
UE Learning from User-generated Data
VL Learning from User-generated Data
UE Machine Learning and Pattern Classification
VL Machine Learning and Pattern Classification
KV Mobile Computing
UE Model Checking
VL Model Checking
KV Multimedia Search and Retrieval
KV Parallel Computing
KV Principles of Cooperation
KV Principles of Interaction
VL Quantum Computing
KV SAT Solving
KV Semantic Data Modeling and Applications
UE Visual Analytics
VL Visual Analytics
IK Operations Research
KS Operations Research
KV Statistical Principles of Data Science
SE Computational Logistics: Metaheuristics
SE Computational Logistics: Optimization