CS-GY 9223: Visualization for Machine Learning
Fall 2025 - Course Introduction & Syllabus
NYU Tandon School of Engineering
2025-09-08
Welcome to Visualization for Machine Learning!
CS-GY 9223 Section N
- Instructor: Claudio Silva (csilva@nyu.edu)
- Time: Mondays 5:00 PM - 7:30 PM
- Location: Jacobs Hall, Room 473
- Office Hours: TBD
Teaching Team
- TA: TBD
- Grader: TBD
- Discord: Join Course
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: TBD
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:
- Understand the role of visualization in the machine learning pipeline
- Design effective visualizations for different types of ML models and data
- Implement interactive visualization systems using D3.js and modern web technologies
- Evaluate ML models through visual analytics
- Create visual explanations for complex ML systems
- Critique existing visualization approaches for ML
- 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 (2.5 hours)
Lectures + Hands-on Programming
- Theory and concepts
- Practical sessions within the block
- Research paper discussions
- Live coding demonstrations
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
We strive to have practical sessions complement theoretical materials within each class meeting.
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
Data Visualization Curriculum - Jeff Heer
Observable NotebooksA Course in Machine Learning - Hal Daumé III
PDF LinkInterpretable Machine Learning - Christoph Molnar
Online Book
Introduction to Machine Learning - Etienne Bernard
Wolfram GuideDeep Learning - Goodfellow, Bengio, Courville
Online VersionUnderstanding 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 3: Team formation
- Week 5: Project proposal due (4 pages)
- Week 8: Mid-term update due (1 page)
- Weeks 14-15: Final presentations
- December 11: 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
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 2025 Schedule - Weeks 1-8
| Week | Date | Topic |
|---|---|---|
| 1 | Sept 2 | Labor Day - No Class |
| 2 | Sept 8 | Course Introduction & Visualization Fundamentals |
| 3 | Sept 15 | Data Types, Encodings & Basic Charts |
| 4 | Sept 22 | Model Assessment & Evaluation |
| 5 | Sept 29 | White Box Methods & Interpretability |
| 6 | Oct 6 | Black Box Methods & Explainable AI |
| 7 | Oct 14 (Tue) | Make-up Class - Dimensionality Reduction |
| 8 | Oct 20 | PCA, t-SNE, UMAP Visualization |
Course Schedule (Tentative) - Part 2
Fall 2025 Schedule - Weeks 9-15
| Week | Date | Topic |
|---|---|---|
| 9 | Oct 27 | Deep Learning Visualization I |
| 10 | Nov 3 | Deep Learning Visualization II |
| 11 | Nov 10 | Advanced Topics & TDA |
| 12 | Nov 17 | Advanced Topics (continued) |
| 13 | Nov 24 | Thanksgiving Break - No Class |
| 14 | Dec 1 | Advanced Topics (continued) |
| 15 | Dec 8 | Final Project Presentations |
Important Dates
- Oct 13: Fall Break - No Monday Class
- Oct 14: Make-up Class (Tuesday)
- Dec 8: Last Class
- Dec 11: Final Project Reports Due
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: Join Course - Primary communication channel
- Office Hours: Will be posted on Discord
- Email: For private matters only
- Course Website: ctsilva.github.io/2025-VisML-CSE/
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
