Marc Peter Deisenroth

Marc Deisenroth is a Lecturer (equivalent to an Assistant Professor in the US) in Statistical Machine Learning at the Department of Computing, Imperial College London. Marc was Program Chair of EWRL 2012, Workshops Chair of RSS 2013 and received Best Paper Awards at ICRA 2014 and ICCAS 2016. He is a recipient of a Google Faculty Research Award and a Microsoft PhD Scholarship. Marc's research interests center around data-efficient and autonomous machine learning.

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Mathematics for Machine Learning: PCA (Coursera) Coursera
Imperial College London

Mathematics for Machine Learning: PCA (Coursera)

Data Science

Dive into the essential mathematics behind one of the most popular dimensionality reduction techniques in machine learning: Principal Component Analysis (PCA). This comprehensive course is designed for those with an intermediate understanding of statistics and linear algebra, aiming to equip you with the skills needed to apply PCA effectively in your data science projects. By the end of this course, you'll have a solid grasp on how to minimize reconstruction error and extract meaningful patterns from high-dimensional datasets.

Sep 21st 2026
4 Weeks
Statistics Analysis Machine Learning
Details
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