Lady Margaret Hall, University of Oxford
Advanced Artificial Intelligence and Machine Learning: Natural Language Processing
Oxford, United Kingdom
Summer Course
DURATION
3 weeks
LANGUAGES
English
PACE
Full time
APPLICATION DEADLINE
EARLIEST START DATE
STUDY FORMAT
Distance Learning, On-Campus
* Applications are processed and offers are made on a rolling basis, and many courses fill up far in advance of the deadline so we recommend applying as early as possible.
Key Summary
About: This intensive programme at Lady Margaret Hall, University of Oxford, teaches natural language processing (NLP) theory and practice. Topics include attention mechanisms, transformers, byte pair encoding, tokenization, large language models (LLMs), prompting and reinforcement learning with human feedback. You’ll study applications like question answering, translation, code generation and language–vision, and examine limitations, bias, ethics and misuse. The course runs 15 July–2 August 2024 and provides grounding and practical experience for research.
Career Outcomes: Graduates can pursue machine learning engineer, NLP researcher or data scientist roles. You'll gain skills in model development, evaluation and responsible AI for tech and research.
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 applications 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.
Dates and Availability
Available as a Residential or Online course on the following dates:
Session 2: 15th July to 2nd August 2024
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 in this course must have experience with 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.
Lady Margaret Hall does not offer scholarships or grants for participation in the LMH Summer Programmes, but many students find they are able to seek financial assistance from their home university or academic department. The best first point of contact is likely the Study Abroad / International Education Office at your university.
By the end of this course, you will:
- Be able to demonstrate an 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.
