---
title: "SU-CS229 Midterm Sheet"
source: https://www.jemoka.com/posts/kbhsu_cs229_midterm_sheet/
---

matrix calculus supervised learning gradient descent Newton&rsquo;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&rsquo;s exponential family some exponential family distributinos: SU-CS229 Distribution Sheet classification take linear regression, squish it: logistic regression; for multi-class, use softmax \(p\qty(y=k|x) = \frac{\exp \theta_{k}^{T} x}{\sum_{j}^{} \exp \theta_{j}^{T} x}\) generative learning modeling each class&rsquo; 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&rsquo;s Inequality feature map and precomputing Kernel Trick Decision Tree boosting
