Visualization for Machine Learning

Spring 2024

Teaching Staff

  • Instructor: Claudio Silva (; webpage

  • Section Leader: Erin McGowan (

  • Teaching Fellow: Vitoria Guardieiro (

  • Grader: Rithvik Guruprasad (


Instruction Mode: In-Person

Meeting Times:

  • DS-GA 3001.001 (Lecture) Thursdays 6:45pm-8:25pm Classroom: 31 Washington Pl (Silver Ctr) Room 520

  • DS-GA 3001.002 (Lab) Fridays 10:15am-11:05am Classroom: 31 Washington Pl (Silver Ctr) Room 520


  • Class Discord:

  • All our communications happen here.

  • Anything related to assignments, grading, etc., send a direct message to Grader(s).

  • Anything related to labs / coding contact Erin or Vitoria.

  • Feel free to direct message for anything else.

Course Prerequisites

  • Solid programming expertise.

  • The coursework includes extensive programming with JavaScript and D3.js. To be covered in the labs.

  • We will also expect students to be able to program in Python.

  • Basic knowlege of machine learning.

Course Description

  • Our course is based on foundations of visual analytics, which is an area of data visualization that is concerned with improving the human analytic process.

  • Visual analytics is concerned with combining automated processes with human-driven processes that are built around data visualization: visual representations of data, and ways to interact with data.

Course Objectives

  • This course is designed to sharpen a student’s knowledge of visualization.

  • We aim to make the student a more effective data scientist.

  • The course has a major project, which will help the student develop research skills.

Course Structure

  • The course include lectures and labs. We will strive to have hands-on sessions to complement theoretical materials.

  • Starts with a short primer on visualization.

  • Then we will cover techniques for visualizing model assessment, white-box and black-box machine learning explainers, and dimensionality reduction.

  • Second half of the course has more advanced topics, including Topology Data Analysis and techniques for visualizing deep neural networks.

Reading Material

  • There is no textbook for the course.

  • We will have suggested reading materials for each class.

  • Supplemental readings to be used as reference material:

    • Data Visualization Curriculum, Heer, link

    • A Course in Machine Learning, Daume, link

    • Interpretable Machine Learning, Molnar, link

    • Introduction to Machine Learning, Bernard, link

    • Deep Learning, Goodfellow et al, link

Research Project

  • Course includes a substantial research project.

  • Projects are expected to be pursued in groups of 2-3.

  • Once the group is finalized, students cannot change or separate their groups throughout the semester.

Course Assessment

  • Assignments (50%)
  • Project Proposal (4-page writeup): 10%
  • Project Updates (1-page writeup): 10%
  • Full Project (up to 8-page writeup): 25%
  • Class Participation: 5%

Late Submissions

Late submissions of assignments will be penalized as follows:

  • A standard deduction rate of 20% per day. It means that after 5 days of being late, your assignment will have a maximum grade of 0 (zero).

  • You will have a one-time exception for submitting assignments late (up to 5 days late).

Academic Integrity

AI policy

  • You can use generative AI tools to do the assignments in this class.

  • If you use an AI to guide you in completing an assignment, you have to disclose which parts were generated by the AI. 

NYU Academic Calendar

  • link to NYU Academic Calendar

  • This course does not have a final exam, but there will be a final project presentation.

  • Also, please pay attention to notable dates such as Add/Drop, Withdrawal, etc.

End of Course Logistics

  • Any questions?

BREAK – 5 minutes

Self Introduction for VisML 2024