Course Info & Policies

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.