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ai deep-dive Reading CMU 07-280

CMU 07-280 Lecture 9: Logistic Regression as Probability Estimation

Lecture 9 models P(y=1|x) with a sigmoid instead of directly predicting 0 or 1, learns parameters with cross-entropy and convex optimization, and extends naturally to softmax regression.

#cmu#ai-course#machine-learning#logistic-regression#classification
ai deep-dive Reading Stanford CS229

Classification and Logistic Regression: Decision Boundaries and Newton's Method

Chapter 2 derives logistic loss from a sigmoid probability model, then contrasts it with the perceptron and extends it through softmax and Newton's method.

#cs229#stanford#machine-learning#logistic-regression#classification