If we feel a cold, then we use a jacket. When we are hungry, we decide to eat. These decisions can be made by us, but how does a machine make a decision? In this chapter, we will learn how to build a decision system that can be implemented on IoT devices. All of these systems can analyze with all the chapters seen in this book. The main idea of this chapter is to use the analytics algorithms, and to learn how to apply them in IoT projects
We will explore the following topics:
- Introduction to decision system and machine learning
- Building a simple decision system-based Bayesian theory
- Integrating a decision system and IoT project
- Building your own decision system-based IoT
A decision system that makes a decision based on several input parameters. A decision system is built on decision theories. Being human involves making decisions for almost all life cases.
The following are examples of decisions that humans make:
- Shall I buy the car today? The decision depends on my preferences. This car looks fine, but it is too expensive for me.
- Shall I bring an umbrella today? This decision depends on the current condition in the area where we are staying. If it is cloudy, it's better to bring an umbrella even though it may not rain.
Generally speaking, we teach a machine such as a computer in order to understand and achieve a specific goal. This case is called machine learning. Varieties of programs are implemented in machines so they can make decisions. Machine learning consists of using various algorithms to build a decision system. In this book, I use fuzzy logic and Bayesian algorithms to make a decision system. I will explain them in the next section.