Lecture Notes

Summary

ML is the ROX

ML is the ROX

Decision Trees (SL1)

Decision Trees Introduction

Expressiveness in Decision Trees

Iterative Dichotomiser 3 (ID3)

Other Considerations

Summary:

• DTs and their representation
• ID3: Top down learning algorithm
• Expressiveness of DTs
• Finding the “best” attributes ($\text{Gain}(S,A)$)
• Dealing with overfitting in DTs

Regression and Classification (SL2)

Introduction to Regression

Choosing the Right Model