Foundations of Information Systems: Course Overview and Key Concepts

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Related lectures (32)

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Distributed Information Systems: Overview and Models

Covers Distributed Information Systems, key tasks, methods, projects, evaluation, and exam support.

Semantic Web: Modeling and Ontologies

Explores the Semantic Web, database schemas, XML data model, and ontologies.

Natural Language Processing: A Primer

Introduces Natural Language Processing (NLP) and its applications, covering tokenization, machine learning, sentiment analysis, and Swiss NLP applications.

Distributed Information Systems: Overview and Challenges

Covers Distributed Information Systems challenges, including autonomy, heterogeneity, trust evaluation, and privacy protection.

Model Representation: Structures and Functions

Covers the representation of mathematical models, functions, and data structures in information systems.

Data Representation: Models and Systems

Discusses data representation through models and systems, covering mathematical models, data structures, levels of modeling, and managing data.

Knowledge Inference

Explores knowledge inference, embedding techniques, and schema matching in data integration.

Deep Learning: Principles and Applications

Covers the fundamentals of deep learning, including data, architecture, and ethical considerations in model deployment.

Data Wrangling with Hive: Managing Big Data Efficiently

Covers data wrangling techniques using Apache Hive for efficient big data management.

Knowledge Representation: Semantics and Data Structures

Explores knowledge representation, data structures, semantics, and the challenges of searching for data on the web.