Course Logistics

• Staff: See the Staff Info page for information about the staff.
• Lectures: Mondays, Wednesdays, Fridays, 9:30am – 10:20am, CSE2 G20.
• Sections: Thursdays. See the Sections page for more information about sections.
• Midterm: 5/1, 9:30am – 10:20am. See the Exams page for more information.
• Final: 6/10, 8:30am – 10:20am. See the Exams page for more information.
• Instructors:
• Natasha Jaques
• TAs:
• Donovan Clay
• Alice Gao
• Claudia Gyonjyan
• Christina Hahn
• Sankar Harilal
• Min Jang
• Leo Maynard-Zhang
• Emmanuel Mensah
• Hanwen Xu

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 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.

Grading:

• Assignments (40%)
  • HW0: 4%
  • HW1-HW4: 36% (9% each)
• Exams (57%)
  • Midterm: 24%
  • Final: 33%
• Section Participation (3%)

Where to get help

• EdStem discussion board:
  • Public/Anonymous Posts
    • Questions like, "Is there a typo in the homework?", "What does this notation mean?", "Is this an accurate description of how this works?".
    • Questions that are not of a personal nature should be posted to the discussion board.
  • Private Posts
    • Questions involving your own code should be posted privately to the EdStem discussion board, not office hours.
    • Personal concerns (like "I was in the hospital", "Laptop was stolen").
• Course staff email cse446-staff@cs.washington.edu (only the professor + 2 admin TAs):
  • Please direct all course-related inquiries to cse446-staff@cs.washington.edu or EdStem. Please do not email the instructors or TAs individually.
  • You do not need to email us to use one of the allowable late days, just go ahead and use it.
  • Personal concerns (like "I was in the hospital", "Laptop was stolen") you aren't comfortable sharing with the entire staff. We highly recommend posting privately to EdStem if you are comfortable.
• TA office hours:
  • Questions like: "How do I get started on problem 2?", "Am I on the right track?".
  • We will not be able to give detailed code debugging help during office hours. It is too time-consuming for such a large class.
• Professor office hours:
  • General conceptual understanding questions about the class, research or career advice.
  • Questions about the homework are better suited to TA office hours.
• Gradescope should be used for all regrade requests.
• Submit anonymous feedback here.

Frequently Asked Questions

I want to register but the class is full. Can I get an add code?

Add codes are given out according to a centralized process organized by CSE. You can reach out to ugrad-adviser@cs.washington.edu or check out the following resources:

• CSE Undergraduate Advising
• CSE Graduate Program Information
• Non-Major Registration Information

What is the formula for curving the courses? Will it be posted?

Curving each course will be based on an affine transformation of scores up to the discretion of the instructors alone and will not be publicly posted.