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.
Date: 28 August 2024
Given by: Sahan Bulathwela
Recording Link: https://videolectures.net/AI_Olympiad_2024_bulathwela_unsupervised_learning/