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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Imperial College London
Imperial College London is a world top ten university with an international reputation for excellence in science, engineering, medicine and business. located in the heart of London. Imperial is a multidisciplinary space for education, research, translation and commercialisation, harnessing science and innovation to tackle global challenges.
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Top-Bewertungen von MATHEMATICS FOR MACHINE LEARNING: PCA
The Programming assignments are quite challenging. The teaching part doesn't equip you with enough resources regarding numpy to get full marks in the Programming Assignments. Good teaching though.
Challenging, but doable. Has some bugs in coding assignments, but clearing them out makes you understand things better. Get ready to spend extra time understanding the concepts.
It is a bit difficult and jumpy. You will need some hard work to fill in the missing links of knowledge which not explicite on the lectrue. Overall, great experience.
The course is generally good but the assignment setting definitely needs to be rectified. Thanks anyway for this course. An important element of machine learning.
Über den Spezialisierung Mathematik für maschinelles Lernen
For a lot of higher level courses in Machine Learning and Data Science, you find you need to freshen up on the basics in mathematics - stuff you may have studied before in school or university, but which was taught in another context, or not very intuitively, such that you struggle to relate it to how it’s used in Computer Science. This specialization aims to bridge that gap, getting you up to speed in the underlying mathematics, building an intuitive understanding, and relating it to Machine Learning and Data Science.
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