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Lecturer(s)
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Matlach Vladimír, Mgr. Ph.D.
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Course content
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Text processing and current NLP tools: The pitfalls of NLP Vector representations of text, vectorization of words, sentences and units (Word2Vec, GloVE, FastText, LASER) Natural language processing and mining tools, libraries for R and Python Language identification Sentiment identification Named entity identification Topic extraction Fundamentals and practical image processing and current approaches: OCR Graphical embeddings Clustering based on graphical similarity Generating labels Social network mining and application of graph algorithms: Identifying key roles in social networks Identifying groups General: Data Mining Roles of data and metadata
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Learning activities and teaching methods
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unspecified
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Learning outcomes
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This course builds on and further develops the previous knowledge from the data processing course in DH. The student will be introduced to advanced applications of already introduced methods to complex data requiring further forms of processing. These include the increasingly large needs to process multimedia data (image, video, text), relational data, and meta-data.
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Prerequisites
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unspecified
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Assessment methods and criteria
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unspecified
Preparation of a seminar paper and a test.
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Recommended literature
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