Section outline

    • The course syllabus.

      Topic
      		 Lectures
      1
      		 Syllabus and obligations
      2 Language and intelligence, a short overview of NLP
      3
      		 Text normalization
      4
      		 Sparse and dense word representations
      5
      		 Neural network and neural embeddings
      6 Convolutional and recurrent neural networks for text
      7
      		 Attention mechanism and transformer networks
      8
      		 Large language models for text classification (BERT)
      9
      		 Large generative language models (GPT and T5 families) and multimodal models
      10
      		 Prompt engineering and retrieval augmented generation
      11
      		 POS-tagging, dependency parsing, named entity recognition and semantic role labelling
      12
      		 Word senses and disambiguation
      13
      		 Affective computing
      14
      		 Machine translation
      16
      		 Summarization and question answering
      17
      		 Knowledge graphs for language
      18
      		 Guest lecture

    • Lecture slides 2023/24 Folder
    • Additional materials Page