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.
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- 5 stars51,12 %
- 4 stars22,60 %
- 3 stars12,73 %
- 2 stars6,64 %
- 1 star6,88 %
Principales reseñas sobre MATHEMATICS FOR MACHINE LEARNING: PCA
Very challenging at times, but very good course none the less. Would recommend to any one who has a solid foundation of Linear Algebra (Course 1) and Multivariate Calculus (Course 2).
Overall the course was great. The only thing was that there was a lot I didn't understand from the videos. The recommended textbook resource was a great help.
This is one hell of an inspiring course that demystified the difficult concepts and math behind PCA. Excellent instructors in imparting the these knowledge with easy-to-understand illustrations.
it's very fantastic course.i enjoyed a lot.i feel reading material should be increases in those courses,others things are perfectly ok.thanks for offering this courses.
Acerca de Programa especializado: Matemática aplicada al aprendizaje automático
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