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This is an archived version of the course.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. You can also find an exercise worksheet there on which you can practise what you will have learned in this lecture.

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

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