Natural Language Processing

Welcome to Natural Language Processing!

Natural language processing (NLP) is a rapidly growing field within the realm of artificial intelligence and computer science. As more and more of our daily interactions and communication are mediated through technology, the ability to accurately and effectively understand and process human language becomes increasingly important. This course is designed to introduce you to the fundamental concepts and techniques of modern NLP. Through a combination of lectures, hands-on exercises, and real-world projects, you will gain a deep understanding of the challenges and opportunities of working with natural language data. You will learn how to apply state-of-the-art algorithms and models to a variety of tasks, including sentiment analysis, text classification, and machine translation. By the end of the course, you will be well-equipped to tackle real-world NLP problems and apply your skills in a variety of settings.

The above was written by ChatGPT! You will learn how to implement the model that powers the GPT family during this course 😉

This webpage will be the main portal for the course. We will link to lecture slides and extra resources in our timetable below. Additionally:

• PDF of slides and optional tutorial materials: available on Scientia
• Announcements and questions/answers/discussions: on Ed

Lectures

Lectures are held every Monday and Thursday. Monday lectures are 16:00 - 18:00. Thursday lectures are 11:00 - 12:00. Lectures start from Week 2 onwards (16th Jan - 9th March) . These will be held at Huxley 308.

Lab

The lab sessions will be held on Thursdays 12:00-13:00 from Weeks 2 to 8 (inclusive). These will be held at Huxley 202, 206, 210, 219, 221, 225.

The lab sessions are for you to work on your coursework and the optional lab exercises. Tutorial helpers will be available to support you and help you with any queries.

Lab Exercises can be found here

Coursework

There is one coursework assignment for this module. We will announce more information in due time.