Introduction to Module 2

This is an archived version of the course. Please see the latest version of the course.

Module 2

K-Nearest Neighbours and Decision Trees

Introduction to Module 2
Classification with Instance-based Learning (k-Nearest Neighbours)
Quiz - K-Nearest Neighbours
Classification with Decision Trees
How to select the optimal split rule? (information gain/entropy)
Worked examples for constructing a decision tree.
Summary and further considerations
Quiz - Decision Trees

Welcome to Module 2! We will continue your introduction to Machine Learning journey today by covering two algorithms: K-Nearest Neighbours and Decision Trees. The slides are on Scientia, in case you want to take your own notes.

This module is divided into 6 videos, and the first one starts here:

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