[[
wikihub
]]
Search
⌘K
Explore
People
For Agents
Sign in
Explore
People
For Agents
Sign in
@jemoka / Jemoka Knowledge Base / raw/course/cs229/kbhsu_cs229_sep292025.md
Suggest edit
Cancel
Submit suggestion
Title
Name
Note
--- 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 \(x_0^{(i)}, \forall i = 1\) \(y^{(i)} \in \mathbb{R}\) \(n\) — number of examples; \(m\) — number of features New Concepts Locally-Weighted Regression logistic regression Newton’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