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Why this course?

  • Background on "The Nature of Code"

Structure / Overview

  • "Research" presentations
  • 3 exercises
  • 1 project

What is Artificial Intelligence?

What is Machine Learning?

  • "A field of study that gives computers the ability to learn without being explicitly programmed." Arthur Samuel, 1957 "Samuel Checkers": the world's first self-learning program.
  • Data --> Meaning
  • Types of Machine Learning
    • Rule-based Systems
    • Supervised Learning
      • Classification, Regression
    • Unsupervised Learning
      • Clustering
    • Reinforcement Learning
    • Generative output

What is Deep Learning?

  • Machine learning with "deep" neural networks.
  • Deep meaning "many layers" deep.

Ok, what about Artificial General Intelligence?

  • DeepMind and Q-Learning

Being Critical

Main references I'm using for today (and beyond)

Overview of Syllabus and Topics

What languages / tools?

  • I will mostly be using Processing and p5.js.
  • You can use anything and everything you like for this course.
  • We will likely branch out into python when using some machine learning frameworks (like tensorflow).

Glossary of terms

Statistics and machine learning glossary from the repo wiki.

Wikipedia links:

Project References

Algorithms

Misc Technical Discussion

Examples and Homework