ANLP: Accelerated Natural Language Processing

Welcome to Accelerated Natural Language Processing

Course summary

This course provides students with a fast-paced introduction to the field of natural language processing: what makes modelling linguistic data different from other areas of machine learning, along with current NLP methods and applications. We cover a range of foundational architectures from deep learning and relevant linguistic phenomena. We focus on what makes automatic processing of language unique and challenging: its statistical properties, complex structure, and pervasive ambiguity. We use English as the primary exemplar throughout but also discuss similarities and differences to other languages, and the implications for NLP models.

The course starts with simple models for text classification and generation. We will then discuss neural models to represent the meaning of words and model language, such as Recurrent Neural Networks and Transformers. We will cover the training pipeline for current Large Language Models, including pre-training, supervised fine-tuning, and alignment with human feedback. As part of the course, we will also introduce methodological and ethical considerations (e.g., evaluation, data collection, algorithmic bias) that are important for research or working in the field.

If you're wondering whether this course is appropriate for you, please see: ANLP: should I take this course?

Changes this year

This course is undergoing major updates for 2025-26! Materials from previous years do not accurately reflect planned course content. In particular, the updated version will no longer cover most of the material on syntax, HMMs, and parsing; instead we will spend much more time on neural methods, including recurrent networks and Transformers.

These changes bring the course more up-to-date with current methods, but they will also be a lot of work for us! Please be understanding if there are occasional glitches.

Where do I find things?

On this site, you will find publicly available information, including all course materials other than the assessed coursework.

On the course Learn site (linked at the top of this page) you will find information that requires a university login:

• all information about assessment, including
  • instructions and downloads
  • due dates and late policy
  • links to submission inboxes
• a link to the course discussion forum