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Machine learning isn't just about writing code; it is deeply rooted in mathematics. Charu Aggarwal’s textbook breaks the curriculum down into two distinct pillars that work in tandem to power modern AI. Part 1: Linear Algebra & Its Applications If you are an active student, check your
This involves moving data from one geometric space to another. It helps us understand how algorithms stretch, rotate, or compress data to find patterns. Charu Aggarwal’s textbook breaks the curriculum down into
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Linear algebra is the language used to represent and manipulate massive datasets. Instead of looking at individual numbers, we look at entire grids of data at once.
If linear algebra represents the data, optimization is the engine that learns from it. Machine learning usually boils down to minimizing an "error" or "loss" function.