Welcome & Course Overview

Optimization: \\(\\min_\\theta J(\\theta)\\) via gradient descent
Probability: \\(P(y|x,\\theta)\\) - probabilistic predictions
Linear Algebra: \\(\\theta = (X^TX)^{-1}X^Ty\\)
Calculus: \\(\\nabla_\\theta \\mathcal{L}\\) - gradients everywhere

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Welcome to CMSC 173

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