Automatic control engineering Books
Taylor & Francis Ltd ServiceOriented Distributed Knowledge Discovery
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Taylor & Francis Ltd Safety and Human Error in Engineering Systems
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Taylor & Francis Ltd Foundations of Predictive Analytics Chapman HallCrc Data Mining and Knowledge Discovery Series
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Taylor & Francis Ltd Feedback Control in Systems Biology
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Taylor & Francis Ltd Applied Genetic Programming and Machine Learning CRC Press International Series on Computational Intelligence
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Taylor & Francis Ltd Introduction to Logistics Engineering
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Taylor & Francis Ltd From Plant Data to Process Control
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Taylor & Francis Ltd Introduction to Wavelet Transforms
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Taylor & Francis Ltd Metaheuristic Computation with MATLAB
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Taylor & Francis Ltd Controls and Automation for Facilities Managers Efficient DDC Systems Implementation
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Taylor & Francis Ltd Networking and Integration of Facilities Automation Systems
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Taylor & Francis Ltd 3D Rotations
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Taylor & Francis Ltd IoT Applications Security Threats and Countermeasures
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Taylor & Francis Ltd Convergence of Blockchain AI and IoT
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Taylor & Francis Ltd Internet of Energy for Smart Cities
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Taylor & Francis Ltd Convergence of Blockchain Technology and EBusiness
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Taylor & Francis Ltd Metaheuristic Computation with MATLAB
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Taylor & Francis Ltd TreeBased Methods for Statistical Learning in R
Book SynopsisTree-based Methods for Statistical Learning in R provides a thorough introduction to both individual decision tree algorithms (Part I) and ensembles thereof (Part II). Part I of the book brings several different tree algorithms into focus, both conventional and contemporary. Building a strong foundation for how individual decision trees work will help readers better understand tree-based ensembles at a deeper level, which lie at the cutting edge of modern statistical and machine learning methodology.The book follows up most ideas and mathematical concepts with code-based examples in the R statistical language; with an emphasis on using as few external packages as possible. For example, users will be exposed to writing their own random forest and gradient tree boosting functions using simple for loops and basic tree fitting software (like rpart and party/partykit), and more. The core chapters also end with a detailed section on relevant softwTrade ReviewTree-based algorithms have been a workhorse for data science teams for decades, but the data science field has lacked an all-encompassing review of trees - and their modern variants like XGBoost - until now. Greenwell has written the ultimate guide for tree-based methods: how they work, their pitfalls, and alternative solutions. He puts it all together in a readable and immediately usable book. You're guaranteed to learn new tips and tricks to help your data science team. -Alex Gutman, Director of Data Science, Author: Becoming a Data Head"Here’s a new title that is a “must have” for any data scientist who uses the R language. It’s a wonderful learning resource for tree-based techniques in statistical learning, one that’s become my go-to text when I find the need to do a deep dive into various ML topic areas for my work."Daniel D. Gutierrez, Editor-in-Chief for insideBIGDATA, USA, insideBIGDATA, February 2023Table of Contents1 Introduction 2 Binary recursive partitioning with CART 3 Conditional inference trees 4 "The hitchhiker’s GUIDE to modern decision trees" 5 Ensemble algorithms 6 Peeking inside the “black box”: post-hoc interpretability 7 Random forests 8 Gradient boosting machines
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Taylor & Francis Ltd Convergence of Blockchain AI and IoT
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Taylor & Francis Ltd Artificial Intelligence in Radiation Oncology and Biomedical Physics
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Taylor & Francis Ltd Supervised Machine Learning
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Taylor & Francis Ltd Artificial Intelligence Applications in Electrical Transmission and Distribution Systems Protection
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Taylor & Francis Ltd Linear and NonLinear System Theory
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Taylor & Francis Ltd Industry 4.0 AI and Data Science
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Taylor & Francis Ltd AI for Cars
Book SynopsisArtificial Intelligence (AI) is undoubtedly playing an increasingly significant role in automobile technology. In fact, cars inhabit one of just a few domains where you will find many AI innovations packed into a single product.AI for Cars provides a brief guided tour through many different AI landscapes including robotics, image and speech processing, recommender systems and onto deep learning, all within the automobile world. From pedestrian detection to driver monitoring to recommendation engines, the book discusses the background, research and progress thousands of talented engineers and researchers have achieved thus far, and their plans to deploy this life-saving technology all over the world.Table of ContentsForeword Preface AI for Advanced Driver Assistance Systems Automatic Parking Traffic Sign Recognition Driver Monitoring System Summary AI for Autonomous Driving Perception Planning Motion Control Summary AI for In-Vehicle Infotainment Systems Gesture Control Voice Assistant User Action Prediction Summary AI for Research & Development Automated Rules Generation Virtual Testing Platform Synthetic Scenario Generation Summary AI for Services Predictive Diagnostics Predictive Maintenance Driver Behavior Analysis Summary The Future of AI in Cars A Tale Of Two Paradigms AI & Car Safety AI & Car Security Summary Further Reading References
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Taylor & Francis Ltd AI for Cars
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Taylor & Francis Ltd Introduction to Geometric Algebra Computing
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Taylor & Francis Ltd Medical Image Processing
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Taylor & Francis Ltd Technology and Tools in Engineering Education
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Taylor & Francis Ltd Industrial Internet of Things
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Taylor & Francis Ltd Machine Learning for Cloud Management
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Taylor & Francis Ltd Robotic Technologies in Biomedical and Healthcare Engineering
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Taylor & Francis Ltd Machine Learning
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Taylor & Francis Ltd Machine Vision for Industry 4.0
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Taylor & Francis Ltd Artificial Intelligence Applications in a Pandemic
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Taylor & Francis Ltd Advanced Dynamics Modeling Duality and Control of Robotic Systems
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Taylor & Francis Ltd Data Mining for Colocation Patterns
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Taylor & Francis Ltd Safety Security and Reliability of Robotic Systems
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Taylor & Francis Ltd Transforming Higher Education Through Digitalization
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Taylor & Francis Ltd Design of Intelligent Applications using Machine Learning and Deep Learning Techniques
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Taylor & Francis Ltd Control Applications of Vehicle Dynamics
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Taylor & Francis Ltd Computational Modeling and Data Analysis in COVID19 Research
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Taylor & Francis Ltd Multimodal Biometric Systems
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Taylor & Francis Ltd Artificial Intelligence
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Taylor & Francis Ltd Machine Learning for Edge Computing
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Taylor & Francis Ltd DataDriven ModelFree Controllers
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Taylor & Francis Ltd Machine Learning for Edge Computing
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Taylor & Francis Ltd Innovative Education Technologies for 21st Century Teaching and Learning
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