- Theory and practice of concepts, methods, and techniques for managing and analyzing large amounts of text data.
- Process and prepare large-scale textual data collections for retrieval and mining.
- Apply retrieval, classification, and clustering methods to a range of information access problems.
- Conduct performance evaluation and error analysis.
- Understanding of the strengths and limitations of modern information retrieval and text mining techniques. Being able to identify promising business applications, participate in and lead such projects.
- Search engine architecture
- Text preprocessing and indexing
- Retrieval models (vector-space model, probabilistic models, learning to rank, neural models)
- Search engine evaluation
- Query modeling, relevance feedback
- Web search (crawling, indexing, link analysis)
- Semantic search (knowledge bases, entity retrieval, entity linking)
- Text clustering
- Text categorization
- Topic analysis
- Opinion mining and sentiment analysis
Required prerequisite knowledge
|Project work||2/5||A - F|
|Written exam||3/5||4 hours||A - F|
Permitted aid: all written and printed material, and basic calculator
Method of work
|Web Search and Data Mining (DAT630_1)||5|
Computer Science - Master's Degree Programme
Industrial Automation and Signal Processing - Master's Degree Programme - 5 year
Link to reading list
Last updated: 15.08.2020