---
title: "SU-CS229 SEP292025"
source: https://www.jemoka.com/posts/kbhsu_cs229_sep292025/
date: 2025-09-29
---

Key Sequence Review even more! Linear Regression, give some intuition, discuss logistic regression and give an optimization method for it.
Notation Recall the notation:
\(\qty(x^{(i)}, y^{(i)})\), ith example \(x^{(i)} \in \mathbb{R}^{m+1}\), where \(x_0^{(i)}, \forall i = 1\) \(y^{(i)} \in \mathbb{R}\) \(n\) &mdash; number of examples; \(m\) &mdash; number of features
New Concepts Locally-Weighted Regression logistic regression Newton&rsquo;s Method parametricity of learning algorithms Non-Parametric Learning Algorithms Parametric Learning Algorithm Important Results / Claims probabilistic intuition for least-squares error Questions Interesting Factoids Scratch
