Advanced Artificial Intelligence and Machine Learning: Large Language Models and Natural Language Processing

About

Through predictive text, translation tools, and smart devices natural language processing (NLP) is increasingly a part of our day-to-day lives, and in large language models like Chat-GPT we see the enormous future potential of this exciting area of research. This advanced course examines the theoretical concepts of NLP and its current and potential future application in diverse domains.

The course begins with an introduction to attention mechanisms, examining self-attention, transformers, and byte pair encoding, before turning to large language models (LLMs) and natural language generation, exploring how they use prompting and reinforcement learning with human feedback. You will look closely at the varied applications of NLP and LLMs in particular, such as question answering, translation, and code generation. In the final part of the course you will discover how language and vision can interact in applications such as video captioning or text to image generation, before looking to the future of NLP research and considering the limitations, biases, ethical concerns, and potential misuses of NLP

This intensive course offers students theoretical understanding and practical experience in a range of natural language processing concepts and techniques, offering career skills as well as excellent foundations for future research.

Course leader

Prof Naeemullah Khan

Target group

This course would suit STEM students with intermediate level experience in artificial intelligence, machine learning, and natural language processing concepts and techniques, including those undertaking, or looking ahead to, graduate level study or research

Course aim

By the end of this course, you will:

- Be able to demonstrate understanding of the algorithms and methods used to process textual data.
- Understand the functionality of large language models and their training through finetuning, low-rank adaptation, and quantized low-rank adaptation.
- Demonstrate understanding of the practical applications of natural language processing.
- Be able to discuss the potential limitations, biases, ethical concerns, and misuses of NLP

Fee info