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Applied Machine Learning /
This comprehensive textbook explores the theoretical underpinnings of learning and equips readers with the knowledge needed to apply powerful machine learning techniques to solve challenging real-world problems. Applied Machine Learning shows, step by step, how to conceptualize problems, accurately...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
New York, N.Y. :
McGraw-Hill Education,
[2019].
|
| Edición: | 1st edition. |
| Temas: | |
| Acceso en línea: | Texto completo |
MARC
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| 100 | 1 | |a Gopal, M., |e author. | |
| 245 | 1 | 0 | |a Applied Machine Learning / |c M. Gopal. |
| 250 | |a 1st edition. | ||
| 264 | 1 | |a New York, N.Y. : |b McGraw-Hill Education, |c [2019]. | |
| 264 | 4 | |c ?2019. | |
| 300 | |a 1 online resource (656 pages) : |b 155 illustrations. | ||
| 336 | |a text |2 rdacontent | ||
| 337 | |a computer |2 rdamedia | ||
| 338 | |a online resource |2 rdacarrier | ||
| 504 | |a Includes bibliographical references and index. | ||
| 505 | 0 | |a Cover -- |t Title Page -- |t Copyright Page -- |t Dedication -- |t About the Author -- |t Contents -- |t Preface -- |t Acknowledgements -- |t 1. Introduction -- |t 1.1 Towards Intelligent Machines -- |t 1.2 Well-Posed Machine Learning Problems -- |t 1.3 Examples of Applications in Diverse Fields -- |t 1.4 Data Representation -- |t 1.5 Domain Knowledge for Productive use of Machine Learning -- |t 1.6 Diversity of Data: Structured/Unstructured -- |t 1.7 Forms of Learning -- |t 1.8 Machine Learning and Data Mining -- |t 1.9 Basic Linear Algebra in Machine Learning Techniques -- |t 1.10 Relevant Resources for Machine Learning -- |t 2. Supervised Learning: Rationale and Basics -- |t 2.1 Learning from Observations -- |t 2.2 Bias and Variance -- |t 2.3 Why Learning Works: Computational Learning Theory -- |t 2.4 Occam?s Razor Principle and Overfitting Avoidance -- |t 2.5 Heuristic Search in Inductive Learning -- |t 2.6 Estimating Generalization Errors -- |t 2.7 Metrics for Assessing Regression (Numeric Prediction) Accuracy -- |t 2.8 Metrics for Assessing Classification (Pattern Recognition) Accuracy -- |t 2.9 An Overview of the Design Cycle and Issues in Machine Learning -- |t 3. Statistical Learning -- |t 3.1 Machine Learning and Inferential Statistical Analysis -- |t 3.2 Descriptive Statistics in Learning Techniques -- |t 3.3 Bayesian Reasoning: A Probabilistic Approach to Inference -- |t 3.4 k-Nearest Neighbor (k-NN) Classifier -- |t 3.5 Discriminant Functions and Regression Functions -- |t 3.6 Linear Regression with Least Square Error Criterion -- |t 3.7 Logistic Regression for Classification Tasks -- |t 3.8 Fisher?s Linear Discriminant and Thresholding for Classification -- |t 3.9 Minimum Description Length Principle -- |t 4. Learning With Support Vector Machines (SVM) -- |t 4.1 Introduction -- |t 4.2 Linear Discriminant Functions for Binary Classification -- |t 4.3 Perceptron Algorithm -- |t 4.4 Linear Maximal Margin Classifier for Linearly Separable Data -- |t 4.5 Linear Soft Margin Classifier for Overlapping Classes -- |t 4.6 Kernel-Induced Feature Spaces -- |t 4.7 Nonlinear Classifier -- |t 4.8 Regression by Support Vector Machines -- |t 4.9 Decomposing Multiclass Classification Problem Into Binary Classification Tasks -- |t 4.10 Variants of Basic SVM Techniques -- |t 5. Learning With Neural Networks (NN) -- |t 5.1 Towards Cognitive Machine -- |t 5.2 Neuron Models -- |t 5.3 Network Architectures -- |t 5.4 Perceptrons -- |t 5.5 Linear Neuron and the Widrow-Hoff Learning Rule -- |t 5.6 The Error-Correction Delta Rule -- |t 5.7 Multi-Layer Perceptron (MLP) Networks and the Error-Backpropagation Algorithm -- |t 5.8 Multi-Class Discrimination with MLP Networks -- |t 5.9 Radial Basis Functions (RBF) Networks -- |t 5.10 Genetic-Neural Systems -- |t 6. Fuzzy Inference Systems -- |t 6.1 Introduction -- |t 6.2 Cognitive Uncertainty and Fuzzy Rule-Base -- |t 6.3 Fuzzy Quantification of Knowledge -- |t 6.4 Fuzzy Rule-Base and Approximate Reasoning -- |t 6.5 Mamdani Model for Fuzzy Inference Systems -- |t 6.6 Takagi-Sugeno Fuzzy Model -- |t 6.7 Neuro-Fuzzy Inference Systems -- |t 6.8 Gentic-Fuzzy Systems -- |t 7. Data Clustering and Data Transformations -- |t 7.1 Unsupervised Learning -- |t 7.2 Engineering the Data -- |t 7.3 Overview of Basic Clustering Methods -- |t 7.4 K-Means Clustering -- |t 7.5 Fuzzy K-Means Clustering -- |t 7.6 Expectation-Maximization (EM) Algorithm and Gaussian Mixtures Clustering -- |t 7.7 Some Useful Data Transformations -- |t 7.8 Entropy-Based Method for Attribute Discretization -- |t 7.9 Principal Components Analysis (PCA) for Attribute Reduction -- |t 7.10 Rough Sets-Based Methods for Attribute Reduction -- |t 8. Decision Tree Learning -- |t 8.1 Introduction -- |t 8.2 Example of a Classification Decision Tree -- |t 8.3 Measures of Impurity for Evaluating Splits in Decision Trees -- |t 8.4 ID3, C4.5, and CART Decision Trees -- |t 8.5 Pruning the Tree -- |t 8.6 Strengths and Weaknesses of Decision-Tree Approach -- |t 8.7 Fuzzy Decision Trees -- |t 9. Business Intelligence and Data Mining: Techniques and Applications -- |t 9.1 An Introduction to Analytics -- |t 9.2 The CRISP-DM (Cross Industry Standard Process for Data Mining) Model -- |t 9.3 Data Warehousing and Online Analytical Processing -- |t 9.4 Mining Frequent Patterns and Association Rules -- |t 9.5 Intelligent Information Retrieval Systems -- |t 9.6 Applications and Trends -- |t 9.7 Technologies for Big Data -- |t Appendix A Genetic Algorithm (GA) For Search Optimization -- |t A.1 A Simple Overview of Genetics -- |t A.2 Genetics on Computers -- |t A.3 The Basic Genetic Algorithm -- |t A.4 Beyond the Basic Genetic Algorithm -- |t Appendix B Reinforcement Learning (RL) -- |t B.1 Introduction -- |t B.2 Elements of Reinforcement Learning -- |t B.3 Basics of Dynamic Programming -- |t B.4 Temporal Difference Learning -- |t Datasets from Real-Life Applications for Machine Learning Experiments -- |t Problems -- |t References -- |t Index. | |
| 520 | 3 | |a This comprehensive textbook explores the theoretical underpinnings of learning and equips readers with the knowledge needed to apply powerful machine learning techniques to solve challenging real-world problems. Applied Machine Learning shows, step by step, how to conceptualize problems, accurately represent data, select and tune algorithms, interpret and analyze results, and make informed strategic decisions. Presented in a non-rigorous mathematical style, the book covers a broad array of machine learning topics with special emphasis on methods that have been profitably employed. Coverage includes: Supervised learning, Statistical learning, Learning with support vector machines (SVM),Learning with neural networks (NN), Fuzzy inference systems, Data clustering, Data transformations, Decision tree learning, Business intelligence, Data mining, And much more. | |
| 530 | |a Also available in print edition. | ||
| 533 | |a Electronic reproduction. |b New York, N.Y. : |c McGraw Hill, |d 2019. |n Mode of access: World Wide Web. |n System requirements: Web browser. |n Access may be restricted to users at subscribing institutions. | ||
| 538 | |a Mode of access: Internet via World Wide Web. | ||
| 546 | |a In English. | ||
| 588 | |a Description based on e-Publication PDF. | ||
| 650 | 0 | |a Machine learning |v Textbooks. | |
| 650 | 0 | |a Mechanical engineering |v Textbooks. | |
| 650 | 0 | |a Machine learning. | |
| 650 | 0 | |a Mechanical engineering. | |
| 650 | 7 | |a TECHNOLOGY & ENGINEERING / Electrical. |2 bisacsh | |
| 655 | 0 | |a Electronic books. | |
| 776 | 1 | 8 | |i Print version: |t Applied Machine Learning. |b First edition. |d New York, N.Y. : McGraw Hill, |c [2019], |z 9781260456844 |
| 856 | 4 | 0 | |u https://accessengineeringlibrary.uam.elogim.com/content/book/9781260456844 |z Texto completo |