CS 194-10, Fall 2011: Introduction to Machine Learning
Reading list




This list is still under construction. An empty bullet item indicates more readings to come for that week.

Readings marked in blue are ones you should cover; readings marked in green are alternatives that are often helpful but probably not essential.

Books


Background reading (review of prerequisite material):

The following stuff is what you "should" already know from Math 53 and 54, CS 70, and CS 188. Just in case some of it has rusted away, the TAs will cover the essentials in the Week 1 discussion section (8/24). Assignment 0 will also help to scrape off the rust, as well as introducing some basic Python tools for the course.

Week 1 (8/25 only): Introduction to machine learning


Week 2 (8/30, 9/1): Linear regression, least squares, methodology


Week 3 (9/6, 9/8): Linear classifiers, logistic regression

For the first lecture (machine learning methodology), the material is covered in the readings for Week 2. For the second lecture (linear classifiers):

Week 4 (9/13, 9/15): Max-margin learning, SVMs, kernels


Week 5 (9/20, 9/22): Decision trees, ensemble learning


Week 6 (9/27, 9/29): Instance-based learning, neural networks


Week 7 (10/4, 10/6): Instance-based learning contd. (10/4 only)


Week 8 (10/11, 10/13):


Week 9 (10/18, 10/20):


Week 10 (10/25, 10/27):


Week 11 (11/1, 11/3):


Week 12 (11/8, 11/10):


Week 13 (11/15, 11/17):


Week 14 (11/22 only):


Week 15 (11/29, 12/1):