Provide skills on systems, methodologies, models, and formalisms for the analysis of structured and unstructured information. In particular, the course aims to present methodological and technological aspects for the extraction, cleaning, integration, analysis, and exploration of information coming from unstructured sources.
teacher profile teaching materials
Data cleaning
Data integration
Towards data semantics and word embeddings
Explainable AI
Xin Luna Dong , Divesh Srivastava: Big Data Integration. Springer ISBN 978-3-031-00725-5
Dan Jurafsky and James H. Martin: Speech and Language Processing (https://web.stanford.edu/~jurafsky/slp3/)
Christopher D. Manning, Prabhakar Raghavan and Hinrich Schütze, Introduction to Information Retrieval, Cambridge University Press (https://nlp.stanford.edu/IR-book/information-retrieval-book.html)
Fruizione: 20810223 INGEGNERIA DEI DATI in Ingegneria informatica e dell'intelligenza artificiale LM-32 (docente da definire)
Programme
Data extractionData cleaning
Data integration
Towards data semantics and word embeddings
Explainable AI
Core Documentation
SlidesXin Luna Dong , Divesh Srivastava: Big Data Integration. Springer ISBN 978-3-031-00725-5
Dan Jurafsky and James H. Martin: Speech and Language Processing (https://web.stanford.edu/~jurafsky/slp3/)
Christopher D. Manning, Prabhakar Raghavan and Hinrich Schütze, Introduction to Information Retrieval, Cambridge University Press (https://nlp.stanford.edu/IR-book/information-retrieval-book.html)
Attendance
Strongly recommended. Attending students must complete the projects assigned by the instructor in groups. Non-attending students must complete the projects assigned by the instructor independently.Type of evaluation
Discussion on projects