Mathematics for Machine Learning: PCA
Master Principal Component Analysis (PCA) and its mathematical foundations for dimensionality reduction in machine learning.
Master Principal Component Analysis (PCA) and its mathematical foundations for dimensionality reduction in machine learning.
This course cannot be purchased separately - to access the complete learning experience, graded assignments, and earn certificates, you'll need to enroll in the full Mathematics for Machine Learning Specialization program. You can audit this specific course for free to explore the content, which includes access to course materials and lectures. This allows you to learn at your own pace without any financial commitment.
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What you'll learn
Implement PCA from mathematical foundations
Master orthogonal projections and inner products
Apply dimensionality reduction to real data
Understand geometric interpretations of PCA
Develop practical Python implementations
Skills you'll gain
This course includes:
2.3 Hours PreRecorded video
11 assignments
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FullTime access
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There are 4 modules in this course
This rigorous course provides a deep mathematical understanding of Principal Component Analysis (PCA), a fundamental dimensionality reduction technique. Students learn essential concepts including dataset statistics, inner products, orthogonal projections, and their geometric interpretations. The curriculum combines theoretical foundations with practical implementation in Python, featuring hands-on programming assignments and real-world applications.
Statistics of Datasets
Module 1 · 4 Hours to complete
Inner Products
Module 2 · 5 Hours to complete
Orthogonal Projections
Module 3 · 3 Hours to complete
Principal Component Analysis
Module 4 · 6 Hours to complete
Fee Structure
Instructor
Marc Deisenroth: Leading Innovations in Statistical Machine Learning at Imperial College London
Marc Deisenroth is a Lecturer (equivalent to an Assistant Professor in the US) in Statistical Machine Learning at the Department of Computing at Imperial College London. He has held prominent roles such as Program Chair of EWRL 2012 and Workshops Chair of RSS 2013, and he has received Best Paper Awards at ICRA 2014 and ICCAS 2016. As a recipient of a Google Faculty Research Award and a Microsoft PhD Scholarship, Marc's research interests focus on data-efficient and autonomous machine learning.
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