CSC411H1 Study Guide - Final Guide: Matrix Calculus, Polynomial, Gradient Descent
Document Summary
Divide data into training / testing examples. Training examples construct function approximator (hypothesis) Maps input x to predict output t. Testing example evaluate hypothesis produced by training data: least-squares regression, 1. Define a model linear: y x = w1 + w: x, 2. Define objective (loss) function squared error btwn y and true t value t 9 y x 9. 9
