AI 539: Machine Learning Challenges in the Real World

How does machine learning perform in the wild?

In this class, we will explore the challenges that machine learning systems face when they move from the laboratory into the real world.

We will be inspired by machine learning applied to problems from astronomy, planetary science, autonomous driving, criminal justice, marketing, etc. Topics will include problem formulation, data collection/labeling, and evaluation techniques, and we will address thorny (but common) obstacles such as missing values, data that is not independently and identically distributed, concept/domain shift, explainability, and more.

You will have the opportunity to apply these concepts and strategies to a data set of your choice. Student work will include reading, implementation, experimentation, analysis of results, and communication of findings. If you're curious about how to solve real problems with machine learning, this is the class for you. Prior experience with supervised machine learning methods (CS 434, CS/AI 534, or instructor permission) is required.

Instructor: Kiri Wagstaff

Class meetings (Winter 2022):
Tuesdays and Thursdays, 2-3:20 p.m. (BEXL 320)

Credits: 3

Syllabus coming soon!

Photo by J. Balla Photography on Unsplash