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Mathematics for Machine Learning: PCA

This intermediate-level course introduces the mathematical foundations to derive Principal Component Analysis (PCA), a fundamental dimensionality reduction technique.
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Statistics of Datasets
Inner Products
Orthogonal Projections
Principal Component Analysis

This intermediate-level course introduces the mathematical foundations to derive Principal Component Analysis (PCA), a fundamental dimensionality reduction technique. We'll cover some basic statistics of data sets, such as mean values and variances, we'll compute distances and angles between vectors using inner products and derive orthogonal projections of data onto lower-dimensional subspaces. Using all these tools, we'll then derive PCA as a method that minimizes the average squared reconstruction error between data points and their reconstruction.

Mathematics for Machine Learning: PCA
Free
per course
Incentives
100% online
Course 3 of 3 in the
Flexible deadlines
Intermediate Level
Approx. 19 hours to complete
English
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