The module aims to provide a rigorous introduction to discrete mathematics, probability, and mathematical statistics for AI and Data Science students. It also enables students to discuss the potential scope of the applications, illustrate typical ways of analysis, and provide an appropriate technical background for related higher-level quantitative modules.
A. Interpret set theory notation, perform operations on sets, and reason about sets; B. Apply basic counting and enumeration methods as these arise in analyzing permutations and combinations; C. Apply basic probability theory to solve related problems D. Provide good knowledge of typical distributions such as Bernoulli, Binomial, Geometric, Uniform, Poisson, Exponential and Normal distributions and their applications; E. Apply Central Limit Theorem for confidence intervals of population means.
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 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 problem-based assessments that are project focused. The delivery pattern provides space in the semester for students to concentrate on completing the assessments. The module will be delivered in a combination of lectures, seminars and labs. Lectures will introduce students to the academic content. Seminars and labs will be used to expand the students understanding of lecture materials. 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. 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.