README.md

Introduction

Welcome and Introduction to Machine Learning

Objectives:

• Define AI, machine learning, and deep learning.
• Overview of applications of machine learning, especially for art and design.
• History of machine learning.
• Overview of tools for machine learning.
• Classification and Regression

Outline:

• Introductions
• Syllabus
• Slides: Introduction to ML
• p5.js review

Reading / Viewing:

• Read What is machine learning? We drew you another flowchart by Karen Hao
• Read A People’s Guide to AI by Mimi Onuoha and Mother Cyborg (Diana Nucera)
• Read Data & Society’s Algorithmic Accountability: A Primer
• Watch Seeing Machines Think — Martin Wattenberg and Fernanda Viégas
• Watch Kyle McDonald - Weird Intelligence

p5.js review

• Learning While Mkaing p5.js with Lauren McCarthy
• Introducing the p5.js web editor!
• p5.js web editor with Cassie Tarakajian
• Code! Programming with p5.js playlist

Assignment

1. Create a blog (or category on a blog) for the course. (You may use any means for publishing your assignments including, but not limited to a GitHub markdown file, google doc, medium post, etc.) This wiki page has resources and information on creating your own blog.
2. Using the A People’s Guide to AI design a scenario for machine learning (it can be as fanstastical as you like). Follow the "Every Day AI activity" on page 23-28 and Embodying Social Algorithms from 36-41 and document your answers to the questions and diagrams in a blog post. Consider the following questions:
   • What are the inputs of your system?
   • What are the outputs of your system?
   • What is the training data? Does the data exist or does it need to be collected?
   • What are the ethical considerations of collecting this data and/or applying this model? Is there a danger that the model will harm individual people or a community? Are there privacy considerations with the data?
3. Post a link to your post on the Homework Wiki Page for Assignment 1