Probability and AI for engineers

What it covers

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, such as analysing radio telescopy data, images and audio signals.

Assessment

Hurdle tasks must be passed regardless of your overall mark.

When and where