This course introduces algorithms and techniques for natural language processing, from computational linguistics for text processing to information extraction for language understanding. The topics include statistical and neural based language modeling, word representation, pretrained language models such as BERT and large language models, such as GPT and Llama. Basic and advanced natural language processing tasks, such as machine translation dialog systems question answering, text classification/labeling/tagging, and knowledge graph will also be introduced.

For a student to pass the course, at least 30% of the maximum mark for the examination must be obtained.