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 goal of this course is 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%.
You are not required to attend sections, but we highly encourage you to do so. It is a great place to get to know your TAs and learn beyond what is taught in lectures.