### Overview

The goal of this class is to provide a broad introduction to machine-learning. The topics covered in this class include some topics in supervised learning, such as k-nearest neighbor classifiers, decision trees, boosting and perceptrons, and topics in unsupervised learning, such as k-means, PCA and Gaussian mixture models. We will also look at some basics of learning theory. The topics covered in this class will be different from those covered in CSE 150.
### Prerequisites

Students are expected to have some familiarity with linear algebra and probability, and should be able to program in some language. Taking CSE 150 is not a prerequisite, but a big plus!
### Textbook

There is no required text for this course. Slides or notes will be posted on the class website. We recommend the following textbooks for optional reading.
- Richard Duda, Peter Hart and David Stork, Pattern Classification, 2nd ed. John Wiley & Sons, 2001.
- Tom Mitchell, Machine Learning. McGraw-Hill, 1997.
- Michael Kearns and Umesh Vazirani, Introduction to Computational Learning Theory, MIT Press, 1997.
- Trevor Hastie, Robert Tibshirani and Jerome Friedman, The Elements of Statistical Learning. Springer, 2009