This introductory module is designed to equip students with fundamental programming skills and knowledge necessary for future AI-related coursework. Students will learn basic Python programming, including the use of essential libraries such as NumPy and Matplotlib, providing a solid foundation for advanced AI topics.
A Understand and apply basic Python programming concepts. B Apply NumPy for numerical computations and data manipulation. C Demonstrate data visualizations using Matplotlib. D Understand and apply fundamental knowledge for data analytics.
The students are assisted during the practical laboratory classes by demonstrators. The teaching philosophy of the module follows very much the philosophy of Syntegrative Education. This philosophy is carried through also in terms of assessment, with a reduction in the use of exams and an increase in coursework, especially problem-based assessments that are project-focused. This module will be delivered through a combination of lectures, group discussions, case studies, hands-on practical exercises, etc. Lectures and group discussions are conducted using the Problem-Based Learning paradigm focusing on student-centred learning, where students develop critical thinking and problem-solving skills to address open-ended problems that lack a straightforward solution. This module is taught with an emphasis on student learning through practice and by projects, facilitated by a module leader, and where appropriate, industrial mentors. Students can identify particular areas of learning needs or interests according to the available project(s). They will conduct independent research to gather information and resources to better define the problem. Progress towards the learning outcomes will be facilitated and monitored, where students are guided to progressively address the given problem through tasks. Independent learning will form an important aspect of the educational activities in this module. Assessed by a project, students shall gain practical experience in undertaking independent study and research on industry-focused real-world problems. The concepts introduced during the lecture are illustrated using step-by-step analysis of example code, complete case studies and live programming tutorials. Each week the students have to solve a set of exercises during the laboratory classes and submit the completed work electronically. This module will leverage generative AI to enhance course content and teaching methods in line with the learning outcomes. By integrating advanced AI technologies, we aim to improve the efficiency of teaching and interaction, while fostering greater student autonomy and flexibility in learning.