Linear Algebra for Data Science by Prof. Wanmo Kang and Prof. Kyunghyun Cho

Authors have been discussing over the past few years how they should teach linear algebra to students in this new era of data science and artificial intelligence.

Over these discussions, which also led to some research collaboration as well, they realized that (one of the central concepts from linear algebra that is used frequently in practice, if not every day, was) the central concepts from linear algebra invoked frequently in practice, if not every day, were projection, and consequently singular value decomposition (SVD) as well as even less frequently positive definiteness.

Unfortunately, they noticed that existing courses on linear algebra often focus much more on the invertibility (or lack thereof), to the point that many concepts are introduced not in the order of their practicality nor usefulness but in the order of the conveniences in mathematical derivations/introductions.

They began to wonder a lot whether they can introduce concepts and results in linear algebra in a radically different way.

So, here’s a new textbook on linear algebra, where they re-imagined how and in which order linear algebra could be taught.

Links:
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