Book Image

Machine Learning with Swift

By : Jojo Moolayil, Alexander Sosnovshchenko, Oleksandr Baiev
Book Image

Machine Learning with Swift

By: Jojo Moolayil, Alexander Sosnovshchenko, Oleksandr Baiev

Overview of this book

Machine learning as a field promises to bring increased intelligence to the software by helping us learn and analyse information efficiently and discover certain patterns that humans cannot. This book will be your guide as you embark on an exciting journey in machine learning using the popular Swift language. We’ll start with machine learning basics in the first part of the book to develop a lasting intuition about fundamental machine learning concepts. We explore various supervised and unsupervised statistical learning techniques and how to implement them in Swift, while the third section walks you through deep learning techniques with the help of typical real-world cases. In the last section, we will dive into some hard core topics such as model compression, GPU acceleration and provide some recommendations to avoid common mistakes during machine learning application development. By the end of the book, you'll be able to develop intelligent applications written in Swift that can learn for themselves.
Table of Contents (18 chapters)
Title Page
Packt Upsell
Contributors
Preface
Index

Seeing association rules


There are many situations where we're interested in patterns demonstrating the co-occurrence of some items. For example, marketers want to know which goods are often bought together, clinical personnel need to know symptoms associated with certain medical conditions, and in information security we want to know which activity patterns are associated with intrusion or fraud. All of these problems have a common structure: there are items (goods, symptoms, records in logs) organized in transactions (shopping list, medical case, user activity transaction). With this type of data, we can then analyze it to find association rules, such as If the client bought a lemon and some cookies, he is also likely to buy tea, or in more formal notation: (cookies, lemon → tea).

Note

We will use pictograms throughout this chapter to facilitate the visual notation of item sets and rules: {

 

 →

}.

These rules allow us to make informed decisions, such as putting associated items on the same...