Professors Wanmo Kang and Kyunghyun Cho have reimagined the way linear algebra is taught to students in the era of data science and artificial intelligence. They realized that key concepts like projection, singular value decomposition (SVD), and positive definiteness are frequently used in practice but often overlooked in traditional linear algebra courses. These courses tend to focus on invertibility and mathematical derivations rather than practicality and usefulness.
To address this issue, Kang and Cho have written a new textbook on linear algebra that introduces concepts in a radically different order. The book covers topics like matrices, vector spaces, orthogonality, and SVD early on, without compromising mathematical rigor. This approach allows students to become proficient in useful results and algorithms from linear algebra after reading just the first few chapters.
The textbook has already been used at the Korea Advanced Institute of Science and Technology (KAIST) and has received feedback that has impacted its organization. The authors believe that their problem-driven approach will prepare students to tackle real-world questions in data science and artificial intelligence.
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