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MLPR class notes

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Background information

Week 1: Introduction to ML with Linear Regression

Week 2: ML fundamentals: generalization, error bars, Gaussians

New Status Page! Check which in-note questions you might have missed.

If you want to get a better idea of what we'll cover, and the style of the notes, last year’s notes are available. We'll cover mostly the same material. Please don't ask questions on last year's notes, but wait until they appear here.

 

 

A coarse overview of major topics covered is below. Some principles aren't taught alone as they're useful in multiple contexts, such as gradient-based optimization, different regularization methods, ethics, and practical choices such as feature engineering or numerical implementation.

You are encouraged to write your own outlines and summaries of the course. Aim to make connections between topics, and imagine trying to explain to someone else what the main concepts of the course are.