Course Logistics
 Instructors:
Kevin Jamieson
 TAs:
 Mohak Bhardwaj, mohakb at cs.washington.edu
 Mahtab Bigverdi, mahtab at cs.washington.edu
 Nuria Alina Chandra, nchand at cs.washington.edu
 Tong Chen, chentong at cs.washington.edu
 Gantcho Dimitrov, gantcho at cs.washington.edu
 Alan Fan, alanfan at cs.washington.edu
 Noah Darwin Feinberg, at cs.washington.edu
 Anderson Lee, lee0618 at cs.washington.edu
 Hannah Lee, hannahyk at cs.washington.edu
 Mingyu Lu, mingyulu at cs.washington.edu
 Esteban Safranchik, estebans at cs.washington.edu
 Guang Yang, gyang1 at cs.washington.edu
 Lecture time and place: Mondays, Wednesdays 9:00  10:20am, CSE2 G20 (Amazon Auditorium)
 EdStem discussion board (TBD)
All questions that are not of a personal nature should be posted to the discussion board.
 Staff can be reached at the email address cse446staff@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 preexisting 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: Your grade will be based on 5 homework assignments: HW0 (8%), HW1 (13%), HW2 (13%), HW3 (13%), HW4 (13%). There will be one midterm worth 20% and a final worth another 20%.
However, depending on whether you are enrolled in 446 or 546, the way the assignments are graded or curved varies (see below).
Course offerings
In the past, CSE 446 was the undergraduate machine learning course, and CSE546 was the graduate version.
Over the years these courses have gotten closer and many undergraduates have opted to take the graduate version
for a more challenging course. At the graduate level, some graduate students have sought a less demanding course to focus on research.
To address the needs of our students, in Autumn 2023 we are offering two different versions of the course concurrently.
A detailed overview of the difference between the courses and eligibility is below.
Course 
Lecture 
Section 
Homework 
Grading 
446 
CSE2 G20 (Amazon Auditorium) MW 9:00  10:20am 
Attend the section you are registered. 
A problems only. No credit will be rewarded for completing B problems. 
You will be graded (e.g., curved) against your peers in 446 only (on a 4.0 scale). For example, if you you recieved a (curved) score of 0.9 on the A problems, then your full grade on your transcript will be (4.0)*(0.9) = 3.6. Any attempt of the B problems will not influence your grade in any way. 
546 
CSE2 G20 (Amazon Auditorium) MW 9:00  10:20am 
None 
A and B problems. 
You will be graded (e.g., curved) against your peers in 546 only.
Your grade on the A and B problems will be curved seperately, and then summed.
For example, if you you recieved a (curved) score of 0.9 on the A problems, and a (curved) score of 0.8 on the B problems, then your full grade on your transcript will be (3.8)*(0.9) + (0.2)*(0.8) = 3.58, rounded to 3.6.
If only the A problems on the homework are attempted, the highest score attainable is a 3.8. If only the B problems are attempted, the highest score attainable is a 0.2.

Frequently Asked Questions
 I am registered for 446, is there any reason I should attempt a B problem? No.
 I am registered for 446, but I recognize that being enrolled in a graduate course may look good for graduate schools or employment in machine learning. Can I switch to 546? Yes! Please contact CSE advisors.
 I was registered for 446, decided to bump up to 546 and am now registered for 546. I now realize I regret the decision, can I switch back to being evaluated as a 446 student? No. You will be evaluated based on whatever course you are officially registered for.
 I am registered for 546. Can I attend a section of 446? Yes, you are welcome to attend any section of your choice. However, please give priority in terms of space and time to those students registered for the section.
 I am registered for 546. I started doing the B problems on the first couple homework assignments then stopped, will I be penalized for not attempting all of them? No. If you attempt any or none of the B problems at any time, your grade on the A problems is always unaffected. Attepting B problems can only help your grade above the 3.8 that is based on the A problems alone.
 I am registered for 546. Do I need to notify instructors that I intend to complete the B problems? No. Just include them on your homework.
 I'm a CSE doctoral student and busy with research so doing the A problems alone is attractive. But I need a 3.4 for the course to count at quals, will I be unfairly judged for not attempting the B problems? No. The instructors are well aware of the grade requirements for graduate students and assign low grades with as much care as any course graded on a 4.0 scale. The only difference is that if you only attempt the A problems, the highest grade you can achieve is 3.8.
 I am enrolled in 446 and my friend is enrolled in 546 but only attempted the A problems. On the A problems we received identical grades on the homeworks. Will the grades on our transcripts be the same? Potentially not. The courses are curved seperately and no attempt will be made to make grades comparable or "fair" across courses in any way.
 What is the formula for curving the courses? Will it be posted? No. 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.