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UNIT-3(Gaussian Mixture Model)-MACHINE LEARNING

Drawbacks of k-means Clustering you will notice that all the clusters created are circular. This is because the centroids of the clusters are updated iteratively using the mean value. Now, consider the following example where the distribution of points is not circular. What do you think will happen if we use k-means clustering on this data? It would still attempt to group the data points circularly. That’s not great! k-means fails to identify the right clusters: Hence, we need a different way to assign clusters to the data points. So instead of using a distance-based model, we will now use a distribution-based model. And that is where Gaussian Mixture Models come into this article! Introduction to Gaussian Mixture Models (GMMs) The Gaussian Mixture Model (GMM) is a probabilistic model used for clustering and density estimation. It assumes that the data is generated from a mixture of several Gaussian components, each representing a distinct clus...

MACHINE LEARNING UNIT-1

UNIT- 1 Syllabus: Introduction - Well-posed learning problems, designing a learning system, Perspectives and issues in machine learning. Concept learning and the general to specific ordering – introduction, a concept learning task, concept learning as search, find-S: finding a maximally specific hypothesis, version spaces and the candidate elimination algorithm, remarks on version spaces and candidate elimination, inductive bias, Gradient Descent Algorithm and its variants. What Is Machine Learning? In the real world, we are surrounded by humans who can learn everything from their experiences with their learning capability, and we have computers or machines which work on our instructions. But can a machine also learn from experiences or past data like a human does? So here comes the role of Machine Learning . What is learning? Learning is any pr...

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