Probability and AI for engineers

This unit will introduce fundamental concepts of probability theory applied to engineering problems in a manner that combines intuition and mathematical precision. The treatment of probability includes elementary set operations, sample spaces and probability laws conditional probability, and independence. A discussion of discrete and continuous random variables common distributions, functions, and expectations forms an important part of this unit. You will also learn the law of large numbers and the central limit theorem.

In the second half of the unit, the focus shifts to practical machine learning techniques. You will gain hands-on experience in supervised learning methods, ranging from decision trees and random forests to regression analysis. The unit also introduces optimisation theory, crucial for understanding the behaviour of learning algorithms. Furthermore, you will learn about data wrangling and the basics of feedforward neural networks. The unit features application examples from various domains to demonstrate the utility of these mathematical tools in real-world scenarios, including analysing radio telescopy data, images and audio signals.

Simulation or virtual practice

Active learning

Problem-based learning

The minimum total expected workload to achieve the learning outcomes for this unit is 144 hours per semester typically comprising a mixture of 3-6 hours of scheduled learning activities and 6-9 hours of independent study per week. Scheduled activities may include a combination of teacher-directed learning, peer-directed learning and online engagement. Independent study may include associated readings, assessment and preparation for scheduled activities.