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Sumario

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Tabla de Contenidos: “…Turaga and Mihaela van der Schaar / /10.1 Introduction: Smarter Planet 234 / /10.2 Illustrative Problem: Transportation 237 / /10.3 Stream Processing Characteristics 238 / /10.4 Distributed Stream Processing Systems 239 / /10.4.1 State of the Art 239 / /10.4.2 Stream Processing Systems 240 / /10.5 Distributed Online Learning Frameworks 244 / /10.5.1 State of the Art 244 / /10.5.2 Systematic Framework for Online Distributed Ensemble Learning 247 / /10.5.3 Online Learning of the Aggregation Weights 250 / /10.5.4 Collision Detection Application 254 / /10.6 What Lies Ahead 257 / /Acknowledgment 258 / /References 258 / /11 Securing the Internet of Things: Need for a New Paradigm and Fog Computing 261 /Tao Zhang, Yi Zheng, Raymond Zheng, and Helder Antunes / /11.1 Introduction 261 / /11.2 New IoT Security Challenges That Necessitate Fundamental Changes to the Existing Security /Paradigm 263 / /11.2.1 Many Things Will Have Long Life Spans but Constrained and Difficult-to-Upgrade Resources 264 / /11.2.2 Putting All IoT Devices Inside Firewalled Castles Will Become Infeasible or Impractical 264 / /11.2.3 Mission-Critical Systems Will Demand Minimal-Impact Incident Responses 265 / /11.2.4 The Need to Know the Security Status of a Vast Number of Devices 266 / /11.3 A New Security Paradigm for the Internet of Things 268 / /11.3.1 Help the Less Capable with Fog Computing 269 / /11.3.2 Scale Security Monitoring to Large Number of Devices with Crowd Attestation 272 / /11.3.3 Dynamic Risk / Benefit-Proportional Protection with Adaptive Immune Security 277 / /11.4 Summary 281 / /Acknowledgment 281 / /References 281 / /INDEX 285.…”

Tabla de Contenidos: “…Intro -- inside front cover -- Machine Learning Bookcamp -- Copyright -- brief contents -- contents -- front matter -- foreword -- preface -- acknowledgments -- about this book -- Who should read this book -- How this book is organized: a roadmap -- About the code -- liveBook discussion forum -- Other online resources -- about the author -- about the cover illustration -- 1 Introduction to machine learning -- 1.1 Machine learning -- 1.1.1 Machine learning vs. rule-based systems -- 1.1.2 When machine learning isn't helpful -- 1.1.3 Supervised machine learning -- 1.2 Machine learning process -- 1.2.1 Business understanding -- 1.2.2 Data understanding -- 1.2.3 Data preparation -- 1.2.4 Modeling -- 1.2.5 Evaluation -- 1.2.6 Deployment -- 1.2.7 Iterate -- 1.3 Modeling and model validation -- Summary -- 2 Machine learning for regression -- 2.1 Car-price prediction project -- 2.1.1 Downloading the dataset -- 2.2 Exploratory data analysis -- 2.2.1 Exploratory data analysis toolbox -- 2.2.2 Reading and preparing data -- 2.2.3 Target variable analysis -- 2.2.4 Checking for missing values -- 2.2.5 Validation framework -- 2.3 Machine learning for regression -- 2.3.1 Linear regression -- 2.3.2 Training linear regression model -- 2.4 Predicting the price -- 2.4.1 Baseline solution -- 2.4.2 RMSE: Evaluating model quality -- 2.4.3 Validating the model -- 2.4.4 Simple feature engineering -- 2.4.5 Handling categorical variables -- 2.4.6 Regularization -- 2.4.7 Using the model -- 2.5 Next steps -- 2.5.1 Exercises -- 2.5.2 Other projects -- Summary -- Answers to exercises -- 3 Machine learning for classification -- 3.1 Churn prediction project -- 3.1.1 Telco churn dataset -- 3.1.2 Initial data preparation -- 3.1.3 Exploratory data analysis -- 3.1.4 Feature importance -- 3.2 Feature engineering -- 3.2.1 One-hot encoding for categorical variables…”