Visualization for Machine Learning Week 1 Lab Recap
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, email@example.com.
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 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.
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.
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:
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