Module
Math and Programming Refreshers for ML
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About
Quick alignment on the linear algebra, multivariable calculus, probability basics, and Python/NumPy skills assumed by undergrad ML courses. Not a full course—targeted refresh tied to ML notation.
Goal
Comfortably manipulate vectors/matrices, compute gradients of simple loss functions, use basic probability (expectation, variance, conditional), and implement vectorized NumPy operations for toy datasets.
Prerequisites
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Tutor
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