Instructor: Anastasia Davydova Lewis | xnastasia on Telegram Location: Situated Intelligence | Online Time: April 20, 2021 12PM PDT Tools: bash, smartphone, computer with internet, RunwayML,Scrapism, or nothing.

Most approaches to teaching machine learning focus on specific models and methodologies. While we are constantly discovering new techniques, one thing remains consistent: the quality of a model’s predictions is always going to depend on the quality of its dataset.

In this workshop, we will design and curate a dataset, and preview what an ML model that you can build yourself would look like.

introductions

overview: mini lecture slide deck

• permanence of bias challenge
• what is bias
• what is scrapism
• what is escrapism why like escapism haha
• how to be honest in avoidant practices
• acceptance of inherent bias in data representation / data vizualization for machines

7 types of Data Bias in Machine Learning

bash tutorial sam lavigne's command line intro

• make a folder
• navigation
• now we need stuff to put in the folder

• What sort of data do you have? Notes, photos, videos, receipts, spreadsheets
• Find data vs Create data
• Label it

manipulating data and labels resizing renaming

• Runway models
• stylegans
• text processing
• utility of different models

more bash

• make some nice representation of the dataset

hanging out

• Hyperdrive Portal from HDSA Becoming a Server
• Training and the Problem of Data
• Daniel Shiffman's Introduction to Runway: Machine Learning For Creators
• Gene Kogan's Machine Learning for Artists
• MIT Unbiased Look at Dataset Bias

From Sam Lavigne's class:

• The Cut Up Method by William Burroughs
• Uncreative Writing by Kenneth Goldsmith
• Digital Divide by Claire Bishop
• Montage by Jared Leibowich