Machine learning (ML) is the scientific study of algorithms and statistical models that computers use in order to perform a specific task effectively without using explicit instructions, relying on patterns and inference instead. It is seen as a subset of artificial intelligence. This course will cover advanced topics in machine learning such as deep learning, transfer learning, multiview learning, clustering and Interpretability of ML methods.
Semantic web technologies (RDF, RDFS, OWL). Ontology and knowledge base development. Data integration and normalization. Ontology matching. Semantic Web access through SPARQL queries. Semantic Web expansion from unstructured data (text), including Named Entity Recognition, Entity Linking and Relation Extraction from textual data. Question Answering over Linked Data. Data availability, redundancy, contextualization and trust.
Basic principles of Information Retrieval. Indexing methods. Query processing. Linguistic aspects of Information Retrieval. Agents and artificial intelligence approaches to Information Retrieval. Relation of Information Retrieval to the World Wide Web. Search engines. Servers and clients. Browser and server side programming for Information Retrieval.