Linear Algebra for Data Science
This course is meant to instill a working knowledge of linear algebra terminology and to lay the foundations of advanced data mining techniques like Principal Component Analysis, Factor Analysis, Collaborative Filtering, Correspondence Analysis, Network Analysis, Support Vector Machines and many more. In order to fully comprehend these important tools and techniques, we will need to understand the language in which they are presented: Linear Algebra. This is NOT a rigorous proof-based mathematics course. It is an intuitive introduction to the most important definitions and concepts that are needed to understand and effectively implement these important data mining methodologies. So that we know how to stir the pile…
Structure of the book
This project is the fusion of a traditional textbook (with definitions, theorems, examples and exercises) with a collection of interactive programming exercises (designed for in-class demonstration) that was engineered to weave practicality and applied problem solving into the curriculum right from the start.
This is a work in progress; please check back frequently for updates.
The author would like to acknowledge and celebrate the work of her PhD advisor Dr. Carl Meyer who wrote the most thorough and complete proof-based presentation of the material in this book. If you’re looking for more details, we strongly suggest the book that contains all of them :
Meyer, Carl D. Matrix analysis and applied linear algebra. Vol. 71. Siam, 2000.