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aiguideReading Stanford CS229 Machine Learning

Maximum Likelihood and Logistic Regression: Why Least Squares Is a Reasonable Choice | Stanford CS229 Lecture 3

Assume the errors are zero-mean, IID Gaussian noise, take the log of the likelihood, and maximum likelihood turns out to be last lecture's least squares. Run the same recipe with 0/1 labels and a sigmoid and you get logistic regression, whose gradient is still "error times input". The lecture closes by comparing Newton's method with SGD: far fewer steps, but each step costs n·d² + d³.

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aideep-diveReading CMU 07-280 AI & ML I

Unifying Logistic and Linear Regression with Maximum Likelihood | CMU 07-280 Lecture 16

Lecture 16 starts from likelihood p(D|θ), uses i.i.d. to factor the joint probability and logs to turn products into sums; Bernoulli MLE yields sample proportions, conditional Bernoulli yields logistic cross-entropy, and Gaussian noise yields squared error.

#cmu#ai-course#machine-learning#maximum-likelihood#probability