It's the same topics as first-year linear algebra, but now done "for real" using algorithms that scale. Part I: SVD = bread and butter in industry with lots of

It's the same topics as first-year linear algebra, but now done "for real" using algorithms that scale. Part I: SVD = bread and butter in industry with lots of applications in ML and stats, engineering stuff too, see https://www.youtube.com/watch?v=R9UoFyqJca8 https://www.youtube.com/watch?v=R9UoFyqJca8 ) Part II: Solving Linear Systems = 70% of science can be describes as A*x=b where A is a matrix and x and b are vectors. Spoiler alert: the "find the RREF algorithm" that we learn in first-year LA is not the most efficient (or numerically stable) option. Part III: Eigenvalues "for real" this time = e.g. iterative solution methods that scale. The flagship application of this idea would be the initial PageRank algorithm that works on the adjacecy matrix representaiton of the graph of all webpages (millions of columns, see http://infolab.stanford.edu/pub/papers/google.pdf http://infolab.stanford.edu/pub/papers/google.pdf ) So to summarize, this course covers all kinds of topics that you don't need to know if you want to apply LA to small and medium data, but for large scale stuff would be really good to know.