Abstract
Machine learning stems from a discipline with a long history, namely, artificial intelligence, which uses statistical techniques to equip computer systems with the ability to "learn" from data, rather than following pre-programmed rules. Over the past few years, machine learning has become one of the workhorses of information technology and has become enmeshed in, albeit usually hidden, every aspect of daily lives. From image recognition to voice recognition, and from predictive maintenance in production lines to health diagnosis systems, the use of machine learning have been embedded everywhere, which invariably accelerates the advancement of most technologies and their applications accordingly.
This chapter intends to provide readers with an overview of machine learning. We will first discuss some fundamentals of probability theory, statistics, and linear algebra, since they are the basics that many machine learning solutions must rely on to become amenable. Next, we provide several use cases of machine learning in IoT solutions. Finally, we will discuss the details of two main categories in machine learning, namely, supervised learning and unsupervised learning.
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Firouzi, F., Farahani, B., Ye, F., Barzegari, M. (2020). Machine Learning for IoT. In: Firouzi, F., Chakrabarty, K., Nassif, S. (eds) Intelligent Internet of Things. Springer, Cham. https://doi.org/10.1007/978-3-030-30367-9_5
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DOI: https://doi.org/10.1007/978-3-030-30367-9_5
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