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

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.

 

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.

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.

Specifically, students on this course must have experience of the following topics:

• Knowledge of the deep learning libraries.
• Understanding of deep learning, recurrent neural networks, GRU, and LSTMs.
• Strong background in optimization and probability.
• Familiarity with the Python programming language.

Dr Khan is an Instructional Assistant Professor in AI at KAUST, where he is part of the KAUST Academy, the continual learning arm of the university. He is also an Associate Research Fellow at Lady Margaret Hall, University of Oxford, and contributes to the LMH Oxford Summer Programmes. Dr Khan was previously a Research Fellow at the Visual Artificial Intelligence Lab at Oxford Brookes University and a Junior Research Fellow at Lady Margaret Hall, University of Oxford. His research interests include Epistemic Artificial Intelligence, Robust Machine Learning, Continual Learning, Invariant/Covariant Descriptor Design in Machine Learning, and Malicious User Detection in Social Networks. Dr Khan has conceived, designed and delivered several courses on Artificial Intelligence and Machine Learning. He leads on Artificial Intelligence and Machine Learning for the LMH Oxford Summer Programmes.

Available as a Residential or Online course on the following dates:

Session 3: 9th August - 27th August 2027

Click below to find out how to apply.

If you have any questions, or would like to know more, please get in touch via the link below.