CS189: Linear algebra review
نویسنده
چکیده
This note is intended to provide the reader with the necessary linear algebra background to mathematically understand several fundamental topics in machine learning we will be discussing in this course, including (but not limited to) principle component analysis (PCA), singular value decomposition (SVD), and spectral clustering. I am assuming the reader is familiar with Math 54 level concepts from linear algebra. The goal here is not to replace any of the excellent references for linear algebra, such as Sheldon Axler’s Linear Algebra Done Right. Rather, the goal is to provide a short and self-contained reference for the specific topics we will use in the course. In preparing this document, I have tried to gather the most concise proofs for each of the results presented. As a result, some level of mathematical “purity” has been sacrificed. For instance, arguments from calculus are used in several places to establish results which are traditionally derived from a purely algebraic point of view.
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