# 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.

## Explainers

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.

- Abstractions Note
- Perceptrons Note QuizSol
- Support Vector Machines Note Quiz Sol
- Convex Optimization Note
- Decision Theory Note
- Discriminant Analysis: LDA, QDA Note Crib QuizSol
- Decompositions: EVD, SVD Note SVD
- Least Squares Note Slides Crib QuizSol
- Ridge Regression Note Slides Crib QuizSol
- Logistic Regression Note
- Bias-Variance Note Crib QuizSol
- Cross Validation Note
- Weighted, Total Least Squares Crib
- Principal Component Analysis Note Crib
- Canonical Correlation Analysis Note
- Nonlinear Least Squares, Gradient Descent Crib
- Neural Networks Note
- Convolutional Neural Networks Note
- Kernels Note
- Nearest Neighbors Note
- Randomized Decision Trees - Note #1 - Note #2
- Clustering Note

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.

## Resources

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

- Fairness in Machine Learning
- Neural Networks : Inspiration in Biology
- Clustering with Gradient Descent
- Kernelized Algorithms
- Kullback-Leibler Divergence
- Closing Remarks from Fall 2016

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.

← *back to* Guide to Undergraduate

Want more tips? Drop your email, and I'll keep you in the loop.