DS-GA 3001: Visualization for Machine Learning

Fall 2026 - Course Introduction & Syllabus

Claudio Silva

NYU Center for Data Science

2026-09-08

Welcome to Visualization for Machine Learning!

DS-GA 3001

  • Instructor: Claudio Silva (csilva@nyu.edu)
  • Lecture (.001): Tuesdays 4:55 PM - 6:55 PM
  • Lab (.002): Tuesdays 7:10 PM - 8:00 PM
  • Location: 60 Fifth Avenue, Room 150 (both)
  • Office Hours: Fridays 1:30-2:30 PM, 370 Jay Street, Room 1153

Teaching Team

  • Session Leader: Bhavya Matam (bm3792@nyu.edu)
  • Grader: Bhavya Matam
  • Discord: Invite link to be posted

About Me

Claudio Silva

  • Institute Professor at NYU
  • Research in visualization, data science, and urban computing
  • Extensive experience teaching VisML and InfoVis courses

Contact

  • Email: csilva@nyu.edu
  • Website: ctsilva.github.io
  • Office Hours: Fridays 1:30-2:30 PM, 370 Jay Street, Room 1153

Course Prerequisites

Essential Requirements

  • Solid programming expertise at graduate level in CS or Data Science
  • JavaScript/D3.js experience helpful but not required - will be covered in class
  • Python programming - expected for ML components
  • Foundation in either data visualization or machine learning

What We’ll Use

  • JavaScript & D3.js
  • Modern web technologies (HTML, CSS, SVG)
  • Python & ML libraries (NumPy, Pandas, Scikit-learn)
  • Git/GitHub for version control
  • Modern web browser
  • Text editor/IDE (VS Code recommended)
  • Node.js for JavaScript development

Warning

If you have no knowledge of machine learning, this course might not be appropriate for you. Please contact the instructor if unsure.

Course Description

Visual Analytics for Machine Learning

This is a research-oriented course on visualization for machine learning, where all students will work on a guided research project.

Visual Analytics Foundation

  • Improving human analytic processes
  • Understanding, reasoning, and decision making with data
  • Combining automated and human-driven processes

Machine Learning Focus

  • Rapid growth in ML drives visualization research
  • Visualization techniques for ML pipelines
  • Interactive visual analytics for model understanding

Fast-Changing Field

This material is at the cutting edge of computing research, bridging visualization and machine learning communities.

Course Learning Objectives

After this course, you will be able to:

  1. Understand the role of visualization in the machine learning pipeline
  2. Design effective visualizations for different types of ML models and data
  3. Implement interactive visualization systems using D3.js and modern web technologies
  4. Evaluate ML models through visual analytics
  5. Create visual explanations for complex ML systems
  6. Critique existing visualization approaches for ML
  7. Develop novel visualization techniques for emerging ML challenges

Overall Goal

Make you a more effective data scientist fluent in the connections between visualization and machine learning.

Course Structure

Weekly Format: Tuesdays - Lecture (2h) + Lab (50 min)

Lecture (4:55-6:55 PM)

  • Theory and concepts
  • Research paper discussions
  • Live coding demonstrations

Lab (7:10-8:00 PM)

  • Hands-on programming
  • Reinforces the lecture you just heard

Course Arc

  • Part 1: Visualization primer
  • Part 2: Model assessment, white/black-box explainers
  • Part 3: Dimensionality reduction (PCA, t-SNE, UMAP)
  • Part 4: Advanced topics (TDA, deep learning visualization)

Tip

The lab follows the lecture the same evening, after a short break, in the same room - so hands-on work always follows the theory it builds on.

Reading Material

No Textbook - Recent Research Focus

Most lectures based on recent technical papers not yet incorporated into textbooks.

Expected Preparation

  • Read corresponding papers prior to each lecture
  • Suggested reading materials provided for each class

Supplemental References

  1. Data Visualization Curriculum - Jeff Heer
    Observable Notebooks

  2. A Course in Machine Learning - Hal Daumé III
    PDF Link

  3. Interpretable Machine Learning - Christoph Molnar
    Online Book

  1. Introduction to Machine Learning - Etienne Bernard
    Wolfram Guide

  2. Deep Learning - Goodfellow, Bengio, Courville
    Online Version

  3. Understanding Deep Learning - Simon J.D. Prince
    Online Book

Research Project

Substantial Research Component

Project Structure

  • Groups of 2-3 students (or solo with permission)
  • Reproduce prior work or implement novel research idea
  • Demonstrate prior work and your project to class
  • No group changes once finalized

Timeline

  • Week 2: Team formation
  • Week 4: Project proposal due (4 pages)
  • Week 7: Mid-term update due (1 page)
  • Weeks 13-14: Final presentations
  • December 14: Final report due (8 pages)

Research Skills Development

This project helps develop research skills through hands-on experience with cutting-edge visualization techniques.

Assessment Overview

No Midterm or Final Exam!

Assignments

50%

Weekly programming assignments

Project Proposal

10%

4-page writeup

Project Updates

10%

1-page writeup

Full Project

25%

8-page writeup + presentation

Participation: 5%

Assignment Details

Programming Focus

Weekly programming assignments for the first half of the semester, focusing on implementing visualization techniques for ML. These build the technical skills needed for the final project.

Late Submission Policy ⏰

Days Late Penalty
1-5 days 20% per day
After 5 days 0 points (maximum grade)

Important

After 5 days late, your assignment will receive a maximum grade of zero.

Important Policies

Academic Integrity

  • Submit your own original work
  • Cite all sources and note collaboration
  • Excessive collaboration beyond discussion is a violation
  • You must be able to explain/re-derive anything you submit

GSAS Statement on Academic Integrity

AI Policy

We embrace AI as a tool, not a replacement

Allowed Uses ✅

  • Learning concepts and debugging
  • Code suggestions and explanations
  • Assignment assistance with disclosure
  • Exploring new techniques

Requirements ⚠️

  • Disclose AI usage in submissions
  • Understand all code you submit
  • Be able to explain your work
  • You’re responsible for errors

Note

AI tools are valuable learning aids, but technical interviews won’t have them available. Balance tool use with fundamental understanding.

Course Schedule (Tentative) - Part 1

Fall 2026 Schedule - Weeks 1-7

Week Date (Tue) Topic
1 Sept 8 Course Introduction & Visualization Fundamentals
2 Sept 15 Perception and Color Theory
3 Sept 22 Model Assessment & Evaluation
4 Sept 29 White-box Methods & Interpretability
5 Oct 6 Black-box Methods & Project Discussion
6 Oct 13 Clustering Visualization
7 Oct 20 Dimensionality Reduction

Course Schedule (Tentative) - Part 2

Fall 2026 Schedule - Weeks 8-14

Week Date (Tue) Topic
8 Oct 27 Deep Learning Visualization
9 Nov 3 NLP and Large Language Model Visualization
10 Nov 10 Topological Data Analysis
11 Nov 17 Time Series and Streaming Data
12 Nov 24 Interpretable ML and Fairness
13 Dec 1 Final Project Presentations I
14 Dec 8 Final Project Presentations II

Important Dates

  • Sept 8: First class (lecture + lab)
  • Sept 15: Add/drop deadline for full-semester classes
  • Nov 25: Deadline to withdraw or request pass/fail
  • Nov 26-27: Thanksgiving Recess (Thu-Fri; our Tuesday sessions are unaffected)
  • Dec 8: Last class (lecture + lab)
  • Dec 14: Final Project Reports Due
  • Dec 15: Reading Day - no classes
  • No final exam for this course

NYU Academic Calendar

Accessibility & Support

Moses Center for Students with Disabilities

  • Contact: mosescsd@nyu.edu
  • Phone: 212-998-4980
  • Location: 726 Broadway, 3rd floor
  • Register for accommodations if needed

Getting Help

  • Discord: Invite link to be posted - Primary communication channel
  • Office Hours: Fridays 1:30-2:30 PM, 370 Jay Street, Room 1153
  • Email: For private matters only
  • Course Website: ctsilva.github.io/2026-VisML-CDS/

Questions?

Any questions about the course?

  • Course structure and objectives
  • Grading policies and timeline
  • Prerequisites and technical requirements
  • Projects and assignments
  • Schedule and important dates

End of Course Logistics

  • Any questions?

BREAK

  • 5 minutes

Self Introduction for VisML 2026

Will be distributed in Discord.