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Kursthemen
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Target audience: The course is recommended for master students of all CS-oriented programs (Master Informatik, Master eXtended AI, Master Business Informatics). Prior knowledge of core NLP and machine learning concepts is desirable, albeit not mandatory.
Credits: 5 ECTS
Lectures: Wed, 10.15-11.45 in Übungsraum I (ÜR I, Informatikgebäude)
Exercises: Fri, 14.15-15.45 in Übungsraum I (ÜR I, Informatikgebäude)
Examination: written or oral exam (depends on the number of students, it will be fixed and announced after the first two weeks of lectures) -
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This is planned/tentative content. As such it is still subject to change, that is, modifications both with respect to the topics and order of lectures are possible.
Date Lecture Blocks 26.4. L1: Languages of the world & Linguistic Universals; Course organization Introduction 10.5. L2: Language modeling, word embedding models, vocabulary & tokenization Block I: Fundamentals 17.5. L3: Training Deep LMs — Gradient Descent, Backprop, Batching, Dropout… Block I: Fundamentals 24.5. L4: Transformer Almighty & Pretraining Language Models Block I: Fundamentals 31.5. L5: Multilingual Word Embedding Spaces Block II: Multilinguality 7.6. L6: Multilingual LMs and CL Transfer, Multilingual Evaluation Block II: Multilinguality 14.6. L7: Curse of Multilinguality, Modularization, and Language Adaptation Block II: Multilinguality 28.6. L8: Transfer for Token-Level Tasks: Word Alignment & Label Projection Block II: Multilinguality 5.7. L9: Neural Machine Translation Block III: Advanced 12.7. L10: Multilingual Sentence Representations Block III: Advanced 19.7. L11: Prompting and LLMs; ChatGPT/GPT-4 Block III: Advanced
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