Visualization for Machine Learning Week 1 Lab Recap

Visualization for Machine Learning Week 1 Lab Recap

Miscellaneous Notes

The slides I showed this week can be found here.

Any questions about what we cover during the lab can be directed to my email,

My office hours are TBD, and will be held online (by popular vote, as opposed to in person on the Brooklyn campus).

Please join the Discord if you haven’t already! This is where all class communication will take place.


We walked through a few notebooks together during this week’s lab, specifically Intro to observable, Intro to HTML/CSS/JS, and a Barplot Example.


We also worked through creating a scatterplot in D3 together. This was originally meant to be an individual activity, but that plan was hindered by Observable sharing permissions (it seems that the custom URL was the issue). You should be able to view and fork your own copy of the activity here now. I was able to access this while logged out, so I believe it should be visible to you all - if this is not the case please @ me in the Discord to let me know.

The answer key to the activity can be found here.

Other Resources

The NYUVIS Guides and Examples page is extremely helpful for all things D3.

Note: Don’t worry about absorbing all of these immediately! We will dive into more complex and interactive visualizations together as the semester continues, but this is a useful reference if you want to create a visualization but aren’t sure where to start.

However, if you are new to Javascript, I would recommend going over the Javascript Basics notebook this week. If you are new to D3, I recommend taking a look at the Data Transformation and SVG and D3 Basics notebooks - we covered some of these functions during the lab this week, but these go into more detail and introduce a few other simple but helpful functions. Building a strong understanding of the basics now will help you in future labs.


Many people asked to see examples of final projects from previous semesters - these will be shared soon once we get closer to the project introduction.

A few people also asked about the topics we’ll be covering this semester, which can be found in the Syllabus (which should be updated with office hour days and times soon). The topics are currently as follows:

Course Schedule

The course schedule is tentative and might need to be adjusted along the way.

Lecture 1: Introduction to Visualization – Part I

Lecture 2: Introduction to Visualization – Part II

Lecture 3: Model Assessment

Lecture 4: White Box Methods

Lecture 5: Black Box Methods

Lecture 6: Dimensionality Reduction

Lecture 7: Project and Research Discussion

Lecture 8 and 9: Topological Data Analysis

Lecture 10: Reserved for Invited Lecture

Lecture 11, 12, and 13: Deep Learning (incl. LLM, Convolutional Nets)

Lecture 14: Advanced Topics