SU-CS229 Midterm Sheet

matrix calculus supervised learning gradient descent Newton’s Method regression y|x,\theta is linear Linear Regression least-squares error: probabilistic intuition for least-squares error in linear regression Normal Equation What if X and y are not linearly related? Generalized Linear Model y|x, \theta can be any distribution that’s exponential family some exponential family distributinos: SU-CS229 Distribution Sheet classification take linear regression, squish it: logistic regression; for multi-class, use softmax p\left(y=k|x\right) = \frac{\exp \theta_{k}^{T} x}{\sum_{j}^{} \exp \theta_{j}^{T} x} generative learning modeling each class’ distributions, and then check which one is more likely: GDA Naive Bayes bias variance tradeoff regularization unsupervised learning k-means clustering Gaussian mixture model and expectation maximization Jensen’s Inequality feature map and precomputing Kernel Trick Decision Tree boosting