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Intelligent Internet of Things pp 243–313Cite as

Machine Learning for IoT

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.

Keywords

  • Artificial intelligence
  • Machine learning
  • Probability theory
  • Linear algebra
  • Supervised learning
  • Unsupervised learning
  • Data preparation
  • Regression analysis
  • Regularization
  • Elastic net regularization
  • Bayesian linear regression
  • Feature selection
  • Entropy
  • Chi-square test
  • Classification
  • Confusion matrix
  • Overfitting and underfitting
  • Over- and undersampling
  • K-nearest neighbor
  • Logistic regression
  • Support vector machine
  • Decision tree classifier
  • Ensembles
  • Random forest
  • Bootstrap aggregating
  • Boosting
  • Artificial neural networks
  • Deep learning
  • Convolution neural networks
  • K-means clustering
  • Hierarchical clustering

Develop a passion for learning. If you do, you will never cease to grow.

Anthony J. D’Angelo

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Author information

Authors and Affiliations

  1. Department of ECE, Duke University, Durham, NC, USA

    Farshad Firouzi

  2. Shahid Beheshti University, Tehran, Iran

    Bahar Farahani

  3. Facebook, Menlo Park, USA

    Fangming Ye

  4. KU Leuven, Leuven, Belgium

    Mojtaba Barzegari

Authors
  1. Farshad Firouzi
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  2. Bahar Farahani
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  3. Fangming Ye
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  4. Mojtaba Barzegari
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Editor information

Editors and Affiliations

  1. Department of ECE, Duke University, Durham, NC, USA

    Farshad Firouzi

  2. Department of ECE, Duke University, Durham, NC, USA

    Krishnendu Chakrabarty

  3. Radyalis LLC, Austin, TX, USA

    Sani Nassif

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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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