General information

Course Logistics

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.

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