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Introduction to Machine Learning

Start by reading the tips below. I've included some lecture-related tips in a post. Note that for a proof-based course like this one, it's more important than ever to understand how a lecture relates to the rest of the content.


I've made some explainer courses, which contain guided practice. Use these for more practical guidance.

Cheat Sheets

Here are resources per topic in the course. After lecture, review the associated crib sheet, and take a quiz with an exam mindset. The notes below are organized using a mixture of different semesters, as each semester's topic coverage and ordering can vary.

Here was the start of a cheat sheet I was assembling, to summarize the decisions associated with machine learning in the wild. Make sure the concepts included here are familiar.


Here are additional notes for special topics from guest lectures or one-off topics specific to a semester.

That's it for this course guide. For your final exams, practice proofs and recall takeaways using the crib sheets above. Good luck!

For a breakdown of the above resources into different semesters, see my CS189 Fall 2016, Spring 2017, or Fall 2017 pages. For more official resources, check out the official CS189 lecture notes.

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