This module aims to provide students with a solid foundation in the key topics underpinning AI, including its historical development, theoretical foundations, basic architecture, modern applications, and ethical implications, as well as a specific focus on Python programming for AI. The module aims to confer an appreciation of the ways in which our world has already been transformed by AI, to explain the fundamental concepts and workings of AI, and to equip us with a better understanding of how AI will shape our society.
A Demonstrate a systematic and critical understanding of Artificial Intelligence foundations. B Systematically recognise the main techniques that have been used in AI, and their range of applicability. C Demonstrate strong expertise and a mastery of practical Python programming skills to implement AI algorithms and systems.
The teaching philosophy of the module follows very much the philosophy of Syntegrative Education. This has meant that the teaching delivery pattern, which follows more intensive block teaching, allows more meaningful contributions 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 problem-based assessments that are project focused. The delivery pattern provides space in the semester for students to concentrate on completing the assessments. This module will be delivered through lectures, tutorials, and labs. The lectures cover the content outlined in the syllabus, providing a comprehensive understanding of image processing and computer vision topics. Tutorials aim to deepen students' comprehension by fostering critical thinking through group discussions and case studies focused on specific topics or research papers. These discussions facilitate exploration of state-of-the-art techniques in image processing and computer vision. Labs are designed to equip students with essential programming skills in module-related areas. Through hands-on exercises and projects, students gain practical experience in implementing and experimenting with image processing and computer vision algorithms. 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.