- The Art of Understanding

• From Numbers to Insights 
• Data, Information, and Knowledge
• Natural Language and Successful Communication
• The Art of Understanding 
• The Principles of Learning

- Basic Machine Learning

• Machine Learning Fundamentals
• How to evaluate a Machine Learning Experiment?
• Evaluation and Generalisation Problems
• Supervised and Unsupervised ML
• Basic ML Algorithms
• Linear Regression
• Decision Trees
• k-means Clustering
• Neural Networks and Deep Learning

- Natural Language Processing

• NLP and Basic Linguistic Knowledge
• NLP Applications, Techniques and Challenges
• Regular Expressions, Tokenisation and Word Normalisation
• Statistical Language Models (N-Gram Model)
• Naive Bayes Text Classification
• Distributional Semantics and Neural Language Models
• Word Embeddings

- Knowledge Graphs

• Knowledge Representations and Ontologies
• Resource Description Framework (RDF) 
• Modeling with RDFS
• Querying RDF(S) with SPARQL
• Popular Knowledge Graphs - Wikidata and DBpedia
• Ontologies with the Web Ontology Language (OWL)
• Linked Data Quality Assurance with SHACL
• The Graph in Knowledge Graphs

- Neurosymbolic AI

• Symbolic and Subsymbolic AI
• Knowledge Graph Embeddings and KG Completion
• The Limits of AI
• KDAI Master Thesis Topics

Learning objectives:

• The students know the fundamentals and measures of information theory and are able to apply those in the context of Knowledge-driven AI.
• The students have basic skills of natural language processing and are enabled to apply natural language processing technology to solve and evaluate simple text analysis tasks.
• The students have fundamental skills of knowledge representation with ontologies as well as basic knowledge of Semantic Web and Linked Data technologies. The students are able to apply these skills for simple representation and analysis tasks.
• The students know the fundamentals of basic machine learning technologies. The students are able to apply these skills for simple prediction and classification tasks.
• The students know the basic principles of neurosymbolic AI.

• Machine Learning:
  G. Rebala, A. Ravi, S. Churiwala, An Introduction to Machine Learning, Springer, 2019. (available via KIT network)
• Natural Language Processing:
  D. Jurafsky, J.H. Martin, Speech and Language Processing, 3rd ed. Draft, 2023.
• Knowledge Graphs:
  A. Hogan, The Web of Data, Springer, 2020. (available via KIT network)