**Course #: **Computer Science 189/289

**Course Title: **Introduction to Machine Learning

**Instructors: **Alexei Efros & Peter Bartlett

**Offering: **Spring 2015

**Location:** 2050 VLSB

**Time:** Tuesdays & Thursdays, 11:00 am - 12:30 pm

**Prerequisites: **Mathematics 53, 54; Computer Science 70; Computer Science 188 or consent of instructor.

**Requirements:**

- Homework will include both traditional written problems as well as programming exercises.
- One midterm exam (on Thursday, March 19, 2015, in the lecture slot) and one final exam.
- CS 289 (only) students must also complete a final course project (due Friday, May 1, 2015. Proposal due Friday, April 3, 2015).

**CS189 Grading:**

- Homework 40%
- Midterm 20%
- Final Exam 40%

**CS289 Grading:**

- Homework 40%
- Midterm 20%
- Final Exam 20%
- Final Project 20%

**Late homework policy:** You have a total of 5 slip days **for the entire course**. Slip days are counted by rounding up (if you miss the deadline by one minute, that counts as 1 slip day). Be cautious with your slip days; don't get stuck with no slip days when your computer crashes the day the last homework is due.

**Textbooks:**

- Trevor Hastie, Robert Tibshirani, and Jerome Friedman, “Elements of Statistical Learning” (2nd edition), Springer, 2009 (pdf online)
- Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani, "An Introduction to Statistical Learning with Applications in R," Springer, 2013 (pdf online)

**Communication with Staff: **Piazza is the place. In general, post a **public** question or note on Piazza. For questions/concerns related to you only, please create a **private **post on Piazza so the course staff can be sure that your post is addressed. If for some reason Piazza is not working for you, of course you can always e-mail the course staff at cs189-staff@lists.berkeley.edu (instructors and GSI's) or e-mail individual staff members.