图书简介

1. Introduction. 1.1. The Internet of Things (IoT) . 1.2. IoT Application Domains. 1.3. IoT Reference Model. 1.4. Performance Evaluation and Modeling of IoT Systems. 1.5. Machine Learning and Statistical Techniques for IoT. 1.6. Overview of the Book. 2. Review of Probability Theory. 2.1. Random Variables. 2.2. Discrete Random Variables. 2.3. Continuous Random Variables 2.4. The Joint Probability Distribution. 3. Simulation Techniques. 3.1. Introduction. 3.2. The Discrete-event Simulation Technique. 3.3. Generating Random Numbers. 3.4. Simulation Designs. 3.5. Estimation Techniques. 3.6. Validation of a Simulation Model. 3.7. Simulation Languages. 4. Hypothesis Testing. 4.1. Statistical Hypothesis Testing for a Mean. 4.2. Analysis of Variance (ANOVA). 5. Multivariable Linear Regression. 5.1. Simple Linear Regression. 5.2. Multivariable Linear Regression. 5.3. An Example. 5.4. Polynomial Regression. 5.5. Confidence and Prediction Intervals. 5.6. Ridge, Lasso, and Elastic Net Regression. 6. Time Series Forecasting. 6.1. A Stationary Time Series. 6.2. Moving Average or Smoothing Models. 6.3. The Moving Average MA(q) Model. 6.4. The Autoregressive Model. 6.5. The Non-seasonal ARIMA (p,d,q) Model. 6.6. Decomposition Models. 6.7. Forecast Accuracy. 6.8. Prediction Intervals. 6.9. Vector Autoregression. 7. Dimensionality Reduction. 7.1. A Review of Eigenvalues and Eigenvectors. 7.2. Principal Component Analysis (PCA). 7.3. Linear and Multiple Discriminant Analysis. 8. Clustering Techniques. 8.1. Distance Metrics. 8.2. Hierarchical Clustering. 8.3. The k-means Algorithm. 8.4. The Fuzzy c-means Algorithm. 8.5. The Gaussian Mixture Decomposition. 8.6. The DBSCAN Algorithm. 9. Classification Techniques. 9.1. The k-nearest Neighbor (k-NN) Method. 9.2. The Naive Bayes Classifier. 9.3. Decision Trees. 9.4. Logistic Regression. 10.: Artificial Neural Networks. 10.1. The Feedforward Artificial Neural Network. 10.2. Other Artificial Neural Networks . 10.3. Activation Functions. 10.4. Calculation of the Output Value. 10.5. Selecting the Number of Layers and Nodes . 10.6. The Backpropagation Algorithm. 10.7. Stochastic, Batch, Mini-batch Gradient Descent Methods. 10.8. Feature Normalization. 10.9. Overfitting. 10.10. Selecting the Hyper-parameters. 11. Support Vector Machines. 11.1. Some Basic Concepts. 11.2. The SVM Algorithm: Linearly Separable Data. 11.3. Soft -margin SVM (C-SVM). 11.4. The SVM Algorithm: Non-linearly Separable Data. 11.5. Other SVM methods. 11.6. Multiple Classes. 11.7. Selecting the Best Values for C and . 11.8. -Support Vector Regression ( -SVR). 12. Hidden Markov Models. 12.1 Markov Chains. 12.2. Hidden Markov Models - An Example. 12.3. The Three Basic HMM Problems. 12.4. Mathematical Notation. 12.5. Solution to Problem 1. 12.6. Solution to Problem 2. 12.7. Solution to Problem 3. 12.8. Selection of the number of states N. 12.9. Forecasting OT t. 12.10. Continuous Observation Probability Distributions. 12.11. Autoregressive HMMs. Appendix A: Some Basic Concepts of Queueing Theory. Appendix B: Maximum Likelihood Estimation (MLE).

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