Table of Contents

What you will learn

• Basics of ML using examples from Literature, Painting, Music, Parlour Games, and Kitchen Recipes

• Develop Intuition for when to use which ML algorithm:

  • Regression
  • Classification
  • Clustering

• Develop Intuition for the underlying Math Structures:

  • Decision Trees leading up to Random Forests
  • Linear Equations, Slopes, Intercepts, and Errors
  • Support Vector Machines and Kernel Tricks

• Doing point-and-click ML using an open source ML tool called Orange (Weblink)

  

• Experience with Wekinator (Weblink) and similar ML apps for Creative Outcomes

• Simple Projects applying ML on your own Data/Research

If time permits:

• Exposure to tinyML using Arduino and other Open Source Electronics

Modules

• Intro to Orange

  Using A Visual drag and drop tool called Orange

• Basics of Machine Learning - Classification

  We will look at the basic models for Classification of Data.

• Basics of Machine Learning - Regression

  We will look at the basic models for Linear Regression.

• Basics of Machine Learning - Clustering

  We will look at the basic models for Clustering of Data.