Aims and Fit of Module

This module provides an in-depth exploration of Large Model Techniques in the fields of Natural Language Processing (NLP) and Computer Vision (CV). Students will gain a comprehensive understanding of state-of-the-art large models such as ChatGPT, AutoGPT, DALL·E, Vision Transformers (ViTs), Segment Anything, and other relevant models. The module covers the underlying principles, architectures, and training methodologies of these models, along with their applications in NLP and CV tasks. Through hands-on projects and practical exercises, students will develop the skills necessary to effectively utilize and adapt large models in real-world scenarios.

This module is suitable for students pursuing advanced studies or research in the fields of AI, as well as professionals interested in leveraging large models for practical applications. Students specializing in AI, machine learning, or computer vision will benefit from this module by acquiring cutting-edge knowledge and skills in Large Model Techniques for NLP and CV. Students aiming to enhance their understanding of AI and explore the latest advancements in large model techniques will find this module valuable for their career growth.

Learning outcomes

A Demonstrate a thorough understanding of the theoretical foundations and concepts behind large model techniques used in different tasks.
B Critically review the application of large models, including language understanding and generation, image recognition, object detection, and image generation.
C Determine comprehensively how to evaluate and measure the performance of large models in natural language processing and computer vision tasks using appropriate metrics and benchmarks.
D Exhibit proficiency in the practical skills required for implementing and deploying large models, including data preprocessing, model training, fine-tuning, and result interpretation.
E Acquire a thorough knowledge and heightened awareness of the ethical and societal implications linked to large model techniques, addressing biases, fairness, privacy issues, and associated risks.

Method of teaching and learning

The teaching philosophy of the module adopts the philosophy of Syntegrative Education. This has meant that the teaching delivery pattern, which follows more intensive block teaching, allows more meaningful contribution from industry partners. This philosophy is carried through also in terms of assessment, with reduction on the use of exams and increase in coursework, especially the use of problem-based assessment which is project focused.

This module will be delivered through lectures, seminars and labs.

Lectures: The module will include lectures delivered by the instructor, focusing on introducing key concepts, theories, and techniques related to large model techniques. Lectures will provide a foundation for understanding the module material and will incorporate real-world examples and case studies to enhance comprehension.

Seminars for Group Discussion and presentation: Seminars will be conducted to facilitate group discussions on specific topics, research papers, or case studies. Students will have the opportunity to critically analyze and debate different perspectives, share insights, and deepen their understanding of large model techniques. Seminars will encourage active participation, critical thinking, and collaboration among students. Students will be assigned topics or research papers related to large model techniques, and they will present their findings to the class. Presentations will provide opportunities for students to showcase their understanding of the material, develop effective communication skills, and engage in peer-to-peer learning and feedback.

Labs: Practical lab sessions will be conducted to allow students to apply the concepts learned in lectures and seminars. In the lab sessions, students will work on hands-on exercises and projects related to implementing and experimenting with large model techniques. These labs will provide students with practical experience, reinforce their learning, and foster problem-solving and technical skills.

Group Projects: Students will participate in group projects that involve the application of large model techniques to solve complex Natural language Processing (NLP) and/or Computer Vision (CV) problems. Working in teams, students will collaborate, apply their knowledge, and develop practical solutions. Group projects will promote teamwork, project management skills, and the integration of various aspects of the module material.

In addition, students will be expected to devote unsupervised time to private study. Private study will provide time for reflection and consideration of lecture material and background reading.