Data Science in Theory and Practice. Maria Cristina Mariani

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target="_blank" rel="nofollow" href="#u190aea23-888f-5239-9a4e-f90ca47d2789">11 Principal Component Analysis 11.1 Introduction 11.2 Background of Principal Component Analysis 11.3 Motivation 11.4 The Mathematics of PCA 11.5 How PCA Works 11.6 Application 11.7 Problems

      18  12 Discriminant and Cluster Analysis 12.1 Introduction 12.2 Distance 12.3 Discriminant Analysis 12.4 Cluster Analysis 12.5 Problems

      19  13 Multidimensional Scaling 13.1 Introduction 13.2 Motivation 13.3 Number of Dimensions and Goodness of Fit 13.4 Proximity Measures 13.5 Metric Multidimensional Scaling 13.6 Nonmetric Multidimensional Scaling 13.7 Problems

      20  14 Classification and Tree‐Based Methods 14.1 Introduction 14.2 An Overview of Classification 14.3 Linear Discriminant Analysis 14.4 Tree‐Based Methods 14.5 Applications 14.6 Problems

      21  15 Association Rules 15.1 Introduction 15.2 Market Basket Analysis 15.3 Terminologies 15.4 The Apriori Algorithm 15.5 Applications 15.6 Problems

      22  16 Support Vector Machines 16.1 Introduction 16.2 The Maximal Margin Classifier 16.3 Classification Using a Separating Hyperplane 16.4 Kernel Functions 16.5 Applications 16.6 Problems

      23  17 Neural Networks 17.1 Introduction 17.2 Perceptrons 17.3 Feed Forward Neural Network 17.4 Recurrent Neural Networks 17.5 Long Short‐Term Memory 17.6 Application 17.7 Significance of Study 17.8 Problems

      24  18 Fourier Analysis 18.1 Introduction 18.2 Definition 18.3 Discrete Fourier Transform 18.4 The Fast Fourier Transform (FFT) Method 18.5 Dynamic Fourier Analysis 18.6 Applications of the Fourier Transform 18.7 Problems

      25  19 Wavelets Analysis 19.1 Introduction 19.2 Discrete Wavelets Transforms 19.3 Applications of the Wavelets Transform 19.4 Problems

      26  20 Stochastic Analysis 20.1 Introduction 20.2 Necessary Definitions from Probability Theory 20.3 Stochastic Processes 20.4 Examples of Stochastic Processes 20.5 Measurable Functions and Expectations 20.6 Problems

      27  21 Fractal Analysis – Lévy, Hurst, DFA, DEA 21.1 Introduction and Definitions 21.2 Lévy Processes 21.3 Lévy Flight Models 21.4 Rescaled Range Analysis (Hurst Analysis) 21.5 Detrended Fluctuation Analysis (DFA) 21.6 Diffusion Entropy Analysis (DEA) 21.7 Application – Characterization of Volcanic Time Series 21.8 Problems

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