Artificial Intelligence and Machine Learning with IoT

For decades, humans have been intrigued by the concept of an intelligent and independent self-learning machine. The idea behind Machine Learning (ML) is to simplify the development of analytical models such that, with the help of available data, algorithms can learn continuously. Internet of Things (IoT)-enabled devices are the major sources of data generation for creating all the data in a variety of ways. Making smart IoT applications involves intelligent processing and analysis of this generated data (Big Data). ML may be used in cases where the desired effect is defined (supervised learning) or where data itself is not defined beforehand (unsupervised learning) or where learning is the outcome of the interaction among the learning model and the environment (reinforcing learning). In this chapter, we present and discuss a classification of machine learning algorithms, which can be used in conjunction with IoT. Furthermore, how different machine learning techniques are applied to derive higher-level information from the data is illustrated. Lastly, we investigate the real-world IoT data characteristics that involve an interpretation of the data.