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Updated in [March 06th, 2023]
Our world is in a data deluge with ever increasing sizes of datasets. Linear algebra is a tool to manage and analyze such data.
This course is part 2 of a 2-part course, with this part extending smoothly from the first. Note, however, that part 1, is not a prerequisite for part 2. In this part of the course, we'll develop the linear algebra more fully than part 1. This class has a focus on data mining with some applications of computer graphics. We'll discuss, in further depth than part 1, sports ranking and ways to rate teams from thousands of games. We'll apply the methods to March Madness. We'll also learn methods behind web search, utilized by such companies as Google. We'll also learn to cluster data to find similar groups and also how to compress images to lower the amount of storage used to store them. The tools that we learn can be applied to applications of your interest. For instance, clustering data to find similar movies can be applied to find similar songs or friends. So, come to this course ready to investigate your own ideas.
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Unlock the Exciting World of Learning! Here's What Awaits You: This course provides learners with the opportunity to explore the applications of linear algebra in data mining, computer graphics, sports ranking, web search, clustering data, and image compression. Learners will gain a deeper understanding of linear algebra and its applications, as well as the ability to apply the methods to their own interests. They will also learn how to use the tools to analyze large datasets, rank teams, search the web, cluster data, and compress images. With this knowledge, learners will be able to apply linear algebra to their own projects and research.
