Machine Learning and Adaptive Intelligence | Fundamental AI Concepts

Gain a strong foundation in machine learning and artificial intelligence with this comprehensive course covering probability theory, supervised and unsupervised learning, and more.

Introduction

This unit aims to provide an understanding of the fundamental technologies underlying modern artificial intelligence. It will provide foundational understanding of probability and statistical modelling, supervised learning for classification and regression, and unsupervised learning for data exploration.

Highlights

• Covers key topics in machine learning and adaptive intelligence, including probability theory, objective functions, linear algebra, regression, basis functions, Bayesian regression, unsupervised learning, Naive Bayes, and Gaussian processes.
• Includes both lectures and lab classes, providing a balance of theoretical knowledge and practical skills.
• Assessed through a combination of submitted practical assignments (70%) and an exam (30%).

Recommendation

This course is recommended for students interested in gaining a strong foundation in the fundamental concepts and techniques of machine learning and artificial intelligence. It is suitable for both undergraduate (COM4509) and postgraduate (COM6509) students.