(starter codes)

• 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.

(starter codes)

• 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

Finished student projects

(starter codes)

• 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
    • Intro to Project 3

• 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

(starter codes)

• 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