Foundations of Natural Language Processing

Catalog description

This course imparts foundational concepts, algorithms, and problem-solving techniques essential for mastering natural language processing (NLP). It delves into key NLP challenges, encompassing both classification and generation problems. The curriculum is structured around computational methods across three stages: pre-deep learning, deep learning, and large language models. Covered topics include word embeddings, sentiment analysis, question answering, dialogue systems, deep learning for NLP, large language models, and standard evaluation metrics. Students will apply acquired knowledge to tackle significant technical issues in real-world NLP tasks and applications. Prospective students are required to complete prerequisite courses to ensure a solid foundation in background knowledge and programming skills, facilitating success in the course.