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Notes

Introduction

A recollection of a few preliminary ideas and developments in Linear Algebra from the perspective of theoretical computer science behind data analysis and machine learning. It is supposed to serve as a reference for some common results and properties, hence, most proofs are omitted. Most of the content is inspired by the book Linear Algebra by Peter D. Lax and a course by Gilbert Strang in Spring 2018 at MIT.

Prerequisites

Section titled “Prerequisites”
  1. Basic algebra: Comfort with solving systems of linear equations, manipulating expressions, isolating variables, and understanding functions.
  2. Basic geometry: Familiarity with Cartesian coordinates and basic geometric concepts.
  3. Functions: Understanding function notation and basic function properties.
  4. Basic calculus: Foundational understanding of rates of change, integrals, and limits.