Master Lab Course - Machine Learning for Natural Language Processing Applications (IN2106, IN4249)
Course 0000004166 in SS 2021
General Data
| Course Type | practical training |
|---|---|
| Semester Weekly Hours | 6 SWS |
| Organisational Unit | Informatics 2 - Chair of Formal Languages, Compiler Construction, Software Construction (Prof. Seidl) |
| Lecturers |
Gerhard Johann Hagerer Edoardo Mosca Maximilian Wich Monika Wintergerst Responsible/Coordination: Georg Groh |
| Dates |
1 singular or moved dates |
Assignment to Modules
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IN2106: Master-Praktikum / Advanced Practical Course
This module is included in the following catalogs:- Further Modules from Other Disciplines
Further Information
Courses are together with exams the building blocks for modules. Please keep in mind that information on the contents, learning outcomes and, especially examination conditions are given on the module level only – see section "Assignment to Modules" above.
| additional remarks | People are increasingly using text-based forms of communication, including social media, to exchange impressions of their everyday life or to get ideas for their individual lifestyle. As a result, a large amount of text data is available online, containing a wealth of information on various topics. This results in new use cases, which have implications for research on machine learning in the field of natural language processing. To this end, recent advances in this technical field appear promising, as they mimic human understanding of text by capturing and extracting universal semantic knowledge from large text resources. Therefore, we offer appropriate research literature, based on which students use state-of-the-art methods. These potentially involve neural networks and include semantic embedding, attention mechanisms, transfer learning, clustering algorithms, and unsupervised and supervised approaches. The lab is divided into five different tracks in terms of content and organization: - Text mining for opinion research on social media - Explainable artificial intelligence for NLP models and applications - Dialogue systems and recipe ingredient substitution for virtual dietary advisors - Hate speech detection in text data from social media - Text summarization NLP models - Knowledge-grounded conversation systems Matching procedure via matching.in.tum.de. - Preliminary meeting: Fr, Feb 5, 14:00 in https://bbb.in.tum.de/geo-d2x-ujh - Deregistration deadline: 31 March 2021 Application survey and slides from preliminary-meeting: https://wiki.tum.de/display/socialcomputing/Application+NLP+Lab+Course+SS2021 |
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| Links |
Course documents E-Learning course (e. g. Moodle) Additional event TUMonline entry |