Visualization for Machine Learning - Spring 2024

Syllabus

Classes

[02/01] Lecture 1: Introduction to Visualization – Part I

• InfoVis slides

[02/08] Lecture 2: Introduction to Visualization – Part II

• Perception slides
• Color slides

[02/15] Lecture 3: Model Assessment

• Model Assessment slides

[02/22] Lecture 4: Visualization for White-box Machine Learning Models

• White Box slides

[02/29] Lecture 5: Visualization for Black-box Machine Learning Models

• Black Box slides

[03/07] Lecture 6: Clustering and Dimensionality Reduction

• Clustering and Dimensionality Reduction slides

[03/14] Lecture 7: Project Discussion

• Project Discussion slides

• Clustering and Dimensionality Reduction (extra) slides

[03/21] Spring Break (no classes)

[03/28] Lecture 8: Invited Lecture - Peter Xenopoulos

• Prediction in Competitive Environments slides

[04/04] Lecture 9: Topological Data Analysis

• Topological Data Analysis slides

[04/11]

[04/18] Lecture 11: Visualization for NLP

• Visualization for NLP slides

[04/25]

[05/02] Project Presentations

Labs

[01/26] Lab 1: Intro to Observable and D3

[02/02] Lab 2: Data Types, Graphical Marks, Visual Encoding Channels, and Gestalt Principles

[02/09] Lab 3: JS and D3 Functions for Data Transformations, Selections and Joins, Linked Views

[02/16] Lab 4: Model Understanding with notebookJS

[02/23] Lab 5: Interpreting White-Box Models

[03/01] Lab 6: Interpreting Black-Box Models

[03/07] Lab 7: Dimensionality Reduction

[03/15] Lab 8: Project Workshop

[03/22] No lab, spring break

[03/29] Lab 9: Topological Data Analysis

[04/05] Lab 10: Mountaineer Presentation by Parikshit Solunke (Topological Data Analysis Part 2)

[04/12] Lab 11: Deep Learning

[04/19] Lab 12: Visualization for NLP

[04/26] NO LAB! Erin will be holding office hours on Zoom instead. Please stop by with any questions/concerns about your final project.

Textbook