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