CS 189/289A Introduction to Machine Learning

https://people.eecs.berkeley.edu/~jrs/189/

 This class introduces algorithms for learning, which constitute an important part of artificial intelligence.
Topics include

• classification: perceptrons, support vector machines (SVMs), Gaussian discriminant analysis (including linear discriminant analysis, LDA, and quadratic discriminant analysis, QDA), logistic regression, decision trees, neural networks, convolutional neural networks, boosting, nearest neighbor search;
• regression: least-squares linear regression, logistic regression, polynomial regression, ridge regression, Lasso;
• density estimation: maximum likelihood estimation (MLE);
• dimensionality reduction: principal components analysis (PCA), random projection, latent factor analysis; and
• clustering: k-means clustering, hierarchical clustering, spectral graph clustering. 

Useful Links

• See the schedule of class and discussion section times and rooms. Attend any section(s) you like.
• Access the CS 189/289A Piazza discussion group.
• If you want an instructional account, you can get one online. Go to the same link if you forget your password or account name.
• Check out this Machine Learning Visualizer by your TA Sagnik Bhattacharya and his teammates Colin Zhou, Komila Khamidova, and Aaron Sun. It's a great way to build intuition for what decision boundaries different classification algorithms find.

Prerequisites

• Math 53 (or another vector calculus course),
• Math 54, Math 110, or EE 16A+16B (or another linear algebra course),
• CS 70, EECS 126, or Stat 134 (or another probability course).

You should take these prerequisites quite seriously: if you don't have them, I strongly recommend not taking CS 189. If you want to brush up on prerequisite material:

• Here's a short summary of math for machine learning written by our former TA Garrett Thomas.
• Stanford's machine learning class provides additional reviews of linear algebra and probability theory.
• There's a fantastic collection of linear algebra visualizations on YouTube by 3Blue1Brown starting with this playlist, The Essence of Linear Algebra. I highly recommend them, even if you think you already understand linear algebra. It's not enough to know how to work with matrix algebra equations; it's equally important to have a geometric intuition for what it all means.
• To learn matrix calculus (which will rear its head first in Homework 2), check out the first two chapters of The Matrix Cookbook.
• Another locally written review of linear algebra appears in this book by Prof. Laurent El Ghaoui.
• An alternative guide to CS 189 material (if you're looking for a second set of lecture notes besides mine), written by our current TA Soroush Nasiriany and our former TA Garrett Thomas, is available at this link. I recommend reading my notes first, but reading the same material presented a different way can help you firm up your understanding. 

Both textbooks for this class are available free online. Hardcover and eTextbook versions are also available.

• Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani, An Introduction to Statistical Learning with Applications in R, Springer, New York, 2013. ISBN # 978-1-4614-7137-0. See Amazon for hardcover or eTextbook.
• Trevor Hastie, Robert Tibshirani, and Jerome Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, second edition, Springer, 2008. See Amazon for hardcover or eTextbook.