# 10-601 Linear Regression

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Jump to navigationJump to searchThis a lecture used in the Syllabus for Machine Learning 10-601B in Spring 2016

### Slides

- William's lecture: Slides in Powerpoint, in PDF.
- Side note: The bias-variance decomposition.

### Readings

- Mitchell 4.1-4.3
- Murphy: 7.1-7.3, 7.5.1
- Optional:
- Bishop 3.1
- There's also a nice but somewhat less technical video lecture on overfitting and bias-variance

### What You Should Know Afterward

- Regression vs. classification
- Solving regression problems with 1 and 2 variables
- Ordinary least squares (OLS) solution (aka normal equations) to linear regression problems
- Gradient descent approach to linear regression
- Data transformation and its impact on the way linear regression is solved, and the expressiveness of LR models