Our educational mission

This course provides an introduction to natural language processing covering morphology, syntax, semantics and pragmatics. Incudes the study of fundamental tasks, algorithms and techniques in some of these areas, and you will conduct experiments with existing resources and tools. The course introduces knowledge-based, statistical, and neural approaches. It also illustrates the use of NLP techniques and tools in several applications and provides insight into many open research problems.

Course begins with some latest, gripping developments in NLP, then covers the fundamentals, and then systematically progressing back toward the latest developments. Includes the study of Generative NLP and LLMs (Large Language Models) and what they can and cannot do. Fundamentals will include Machine Learning topics such as classification, clustering, Bayesian probability, Hidden Markov Models (HMMs), Conditional Random Fields (CRFs), Deep Learning and Knowledge Representation and Reasoning Methods such as Answer Set Programming.