CS-GY 9223: Selected Topics in CS - Visualization for Machine Learning

Instructor: Claudio Silva (csilva@nyu.edu)
Teaching Assistant: Parikshit Solunke (pss442@nyu.edu)
Meeting Time: Mondays 5:00 PM - 7:30 PM
Classroom: Jacobs Hall, 6 Metrotech Room 473, Brooklyn Campus
Make-up Class: Tuesday, October 14 (for Fall Break)

Announcements

⚠️ Please Note: Course schedule, assignments, and materials are tentative and subject to updates during the semester. Students will be notified of any changes via Discord and course announcements.

Course materials will be posted as the semester progresses

Upcoming Classes

Week 1 (Sept 2) - Labor Day

• No Class - Labor Day Holiday

Week 2 (Sept 8)

• Topics: Course Introduction, Syllabus, Introduction to Visualization, Hands-on Vega-Lite
• Materials:
  • Course Introduction & Syllabus
  • Information Visualization Introduction
  • Vega-Lite Lab
• Assignment: TBD

Week 3 (Sept 15)

• Topics: TBD
• Materials: TBD

Assignments

Weekly Assignments (50% of grade)

• Exercise 1: Visualization critique - Due Sept 15
• Exercise 2: Color palette design - Due Sept 22
• More programming assignments throughout first half of semester

Research Project (45% of grade)

• Team formation - Week 3
• Project Proposal (4-page writeup) - Week 5 - 10%
• Project Updates (1-page writeup) - Week 8 - 10%
• Final Project (8-page writeup + presentation) - Weeks 14-15 - 25%

Class Participation (5% of grade)

Quick Links

• Discord: Join Course Discord
• Brightspace: [Course materials and submissions]
• Office Hours: TBD

Course Description

This course explores the intersection of visualization and machine learning, focusing on how visualization techniques can help understand, debug, and improve machine learning models. Students will learn to create visual analytics systems for model assessment, feature analysis, and result interpretation. Topics include visualization for model performance, feature importance, clustering, dimensionality reduction, deep learning architectures, and interpretable AI.

Prerequisites

• Solid programming skills (Python and JavaScript)
• Basic knowledge of machine learning concepts
• Familiarity with web technologies (HTML, CSS) helpful but not required