PSAM
5020

Machine Learning

Parsons School of Design: School of Art, Media, and Tech

Non-Liberal Arts
Undergraduate Course
Graduate Course
Degree Students
AI and Machine Learning
Spring 2026
Taught By: Thiago Hersan
Section: A

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.

Open to: All university graduate degree students and upper-level undergraduate degree students. Some seats have been reserved for Data Visualization students.
Prerequisites: No Prerequisites
Co-Requisites: No Co-requisites

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

Meeting Info:
Days: Wednesday
Times: 12:10pm - 2:50pm
Building: TBD
Room: TBD
Date Range: 1/21/2026 - 5/12/2026
Machine Learning
Fall 2025
Taught By: Thiago Hersan
Section: A

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.

Open to: All university graduate degree students. Some seats have been reserved for Data Visualization students.
Prerequisites: No Prerequisites
Co-Requisites: No Co-requisites

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

Meeting Info:
Days: Monday
Times: 4:00pm - 6:40pm
Building: 66 5th Ave
Room: 507
Date Range: 8/27/2025 - 12/15/2025