Webinar 11: Unsupervised Learning

In the real world, not every dataset we work on has a target variable. This kind of data cannot be analysed using supervised learning algorithms. In such scenarios, we need the help of unsupervised learning. One of the most popular types of analysis under unsupervised learning is Clustering. Clustering is used when the goal is to group similar data points in a dataset. In this lecture, we familiarise ourselves with the idea behind clustering using a foundational clustering algorithm called K-means clustering. In the latter part of the lecture, we will extend our understanding of clustering by analysing a probabilistic algorithm that performs the same task, the Gaussian mixture model.

Sahan is a lecturer affiliated with the Centre for Artificial Intelligence at University College London (UCL) currently contributing to X5GON and HumaneAI projects. He is the deputy programme director of the AI for Sustainable Development MSc programme at UCL, the first programme of its kind in the world and part of the UNESCO Chair of Artificial Intelligence. His research interests lie in the theme: “Improving AI-enabled systems for lifelong learning”. Before joining UCL, he has worked in several research roles in the industry in cybersecurity and personalised advertising domains where he gained experience in user state modelling in a big data landscape.