General information

• Instructors:
Kevin Jamieson and Anna Karlin
• TAs:
Satvik Agarwal, Kousuke Ariga, Eric Chan, Benjamin Evans, Shobhit Hathi, Zeyu Liu, Andrew Luo, Vardhman Mehta,
Cheng Ni, Deric Pang, Robbie Weber, Kyle Zhang and Michael Zhang

• Time and place: Mondays and Wednesdays 3:30 -- 4:50pm, Guggenheim Hall (GUG) , Room 220
• Piazza discussion board
The access code is on canvas.
All questions that are not of a personal nature should be posted to the discussion board.
• Staff can be reached at the email address cse446-staff@cs.washington.edu.
• Submit anonymous feedback here

About the Course, Prerequisites and Grading

Machine learning explores the study and construction of algorithms that can learn from historical data and make inferences about future outcomes. This study is a marriage of algorithms, computation, and statistics so this class will be have healthy doses of each. The goals of this course are to provide a thorough grounding in the fundamental methodologies and algorithms of machine learning.

Prerequisites: Students entering the class should be comfortable with programming and should have a pre-existing working knowledge of linear algebra (MATH 308), vector calculus (MATH 126), probability and statistics (CSE 312/STAT390), and algorithms. For a brief refresher, we recommend that you consult the linear algebra and statistics/probability reference materials on the Textbooks page.

Test dates:

• Midterm in class on May 8
• Final on Thursday, June 13 from 2:30-4:20 p.m. See information here.

Grading: Your grade will be based on 5 homework assignments (50%), a midterm (20%), and a final (30%).