Machine Learning
Parsons School of Design: School of Art, Media, and Tech
CRN: 3871
Credits: 3
Any student interested in AI and machine learning should take this course. Machine learning is about understanding how algorithms learn from experience and improve over time. In this class, students will explore how data can be used to uncover patterns, make predictions, and better understand complex information. We will look at both supervised and unsupervised methods, including classification, regression, clustering, subgroup analysis, and association models. The course also emphasizes contemporary techniques that make complex data easier to visualize and understand.
College: Parsons School of Design (PS)
Department: School of Art, Media, and Tech (AMT)
Campus: New York City (GV)
Course Format: Studio (S)
Modality: In-Person
Max Enrollment: 15
Repeat Limit: 2
Add/Drop Deadline: February 3, 2026 (Tuesday)
Online Withdrawal Deadline: April 14, 2026 (Tuesday)
Seats Available: Yes
Status: Open*
* Status information is updated every few minutes. The status of this course may have changed since the last update. Open seats may have restrictions that will prevent some students from registering. Updated: 6:08am EDT 10/15/2025
CRN: 18432
Credits: 3
Any student interested in AI and machine learning should take this course. Machine learning is about understanding how algorithms learn from experience and improve over time. In this class, students will explore how data can be used to uncover patterns, make predictions, and better understand complex information. We will look at both supervised and unsupervised methods, including classification, regression, clustering, subgroup analysis, and association models. The course also emphasizes contemporary techniques that make complex data easier to visualize and understand.
College: Parsons School of Design (PS)
Department: School of Art, Media, and Tech (AMT)
Campus: New York City (GV)
Course Format: Studio (S)
Modality: In-Person
Max Enrollment: 15
Repeat Limit: 2
Add/Drop Deadline: September 9, 2025 (Tuesday)
Online Withdrawal Deadline: November 17, 2025 (Monday)
Seats Available: Yes
* Seats available but reserved for a specific population.
Status: Closed*
* Status information is updated every few minutes. The status of this course may have changed since the last update. Open seats may have restrictions that will prevent some students from registering. Updated: 6:08am EDT 10/15/2025