Spring 2024 - Teaching Materials (Syllabus)
- Introduction to ADS
- Project 1 description(Github Classroom Link)
- Tutorial 1 R Notebook + Example: Repo | Knitted HTML R Notebook | Presentation + More Examples: Repo1, Repo2, Repo3, Repo4 | An example on Jupyter Notebook
- A tutorial on GitHub | Introduction to GitHub
- Recap on last week
- Overview of starter codes
- An example R notebook on presidential speeches (HTML)
- Interactive Word Cloud
- Intro to word2vec slides | R Notebook tutorial for word2vec | Jupyter Notebook Tutorial for word2vec
- Submission and presentation for project 1
- Discussion and Q&A
- Project 1 presentations.
- Project 2 starts.
- Check Piazza for your project team and follow the video instructions to clone the starter codes.
- After you join project 2, you can clone your team's GitHub repo to your local computer.
- You can find in the starter codes:
- the project description,
- an example toy shiny app.
- Spatial data visualization
- Tutorial on project 2 - Introduction to shiny app (app)
- A note on contribution
- Shiny Tutorial (zipped folder) (online link)
- Intro to OpenFEMA
- Shiny Examples from previous semesters (Example 1: Online, Repo; Example 2: Online, Repo)
- Peer review of Project 1
- Discussion and Q&A
- Tutorial on SQL in R(zipped folder)
- Tutorial on RShiny Deployment in GCP, Slides
- Tutorial on giving presentations
- Discussion on project 2
- Project 2 presentations
- Project 3 starts.
- Check Piazza for your project team and follow the video instructions to clone the starter codes.
- After you join project 3, you can clone your team's GitHub repo to your local computer.
- You can find in the starter codes
- Recap on project 3 requirements and starter codes.
- Tutorials + Q&A
- Tutorials: Basic Image Analysis in Python, in R (zipped folder)
- Slides on Weakly Supervised Learning
- Overview on Weakly Supervised Learning
- Overview on predictive modeling
- Tutorial on basic neural network
- Project 3 reminders (on piazza)
- Discussion
- Project 3 submission and presentations
- Introduction to Project 4
- Machine Learning Fairness Introduction (slides used in class)
- Updated slides: Methods for Machine Learning Fairness | Previous slides: Overview of the Methods from the reference papers (hand-written notes)
- Find your group for project 4 and clone the template repository following the video instructions.
- Talk on fairness (see slides)
- Overview on the methods
- Method assignment on Piazza
- Live class canceled
- Feel free to have group brainstorm and meetings offline
- Project 5 discussions
- Project 4 presentations