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...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Gopal, M. (Autor)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: New York, N.Y. : McGraw-Hill Education, [2019].
Edición:1st edition.
Temas:
Machine learning > Textbooks.
Mechanical engineering > Textbooks.
Machine learning.
Mechanical engineering.
TECHNOLOGY & ENGINEERING / Electrical.
Electronic books.
Acceso en línea:Texto completo
Tabla de Contenidos:
  • Cover
  • Title Page
  • Copyright Page
  • Dedication
  • About the Author
  • Contents
  • Preface
  • Acknowledgements
  • 1. Introduction
  • 1.1 Towards Intelligent Machines
  • 1.2 Well-Posed Machine Learning Problems
  • 1.3 Examples of Applications in Diverse Fields
  • 1.4 Data Representation
  • 1.5 Domain Knowledge for Productive use of Machine Learning
  • 1.6 Diversity of Data: Structured/Unstructured
  • 1.7 Forms of Learning
  • 1.8 Machine Learning and Data Mining
  • 1.9 Basic Linear Algebra in Machine Learning Techniques
  • 1.10 Relevant Resources for Machine Learning
  • 2. Supervised Learning: Rationale and Basics
  • 2.1 Learning from Observations
  • 2.2 Bias and Variance
  • 2.3 Why Learning Works: Computational Learning Theory
  • 2.4 Occam?s Razor Principle and Overfitting Avoidance
  • 2.5 Heuristic Search in Inductive Learning
  • 2.6 Estimating Generalization Errors
  • 2.7 Metrics for Assessing Regression (Numeric Prediction) Accuracy
  • 2.8 Metrics for Assessing Classification (Pattern Recognition) Accuracy
  • 2.9 An Overview of the Design Cycle and Issues in Machine Learning
  • 3. Statistical Learning
  • 3.1 Machine Learning and Inferential Statistical Analysis
  • 3.2 Descriptive Statistics in Learning Techniques
  • 3.3 Bayesian Reasoning: A Probabilistic Approach to Inference
  • 3.4 k-Nearest Neighbor (k-NN) Classifier
  • 3.5 Discriminant Functions and Regression Functions
  • 3.6 Linear Regression with Least Square Error Criterion
  • 3.7 Logistic Regression for Classification Tasks
  • 3.8 Fisher?s Linear Discriminant and Thresholding for Classification
  • 3.9 Minimum Description Length Principle
  • 4. Learning With Support Vector Machines (SVM)
  • 4.1 Introduction
  • 4.2 Linear Discriminant Functions for Binary Classification
  • 4.3 Perceptron Algorithm
  • 4.4 Linear Maximal Margin Classifier for Linearly Separable Data
  • 4.5 Linear Soft Margin Classifier for Overlapping Classes
  • 4.6 Kernel-Induced Feature Spaces
  • 4.7 Nonlinear Classifier
  • 4.8 Regression by Support Vector Machines
  • 4.9 Decomposing Multiclass Classification Problem Into Binary Classification Tasks
  • 4.10 Variants of Basic SVM Techniques
  • 5. Learning With Neural Networks (NN)
  • 5.1 Towards Cognitive Machine
  • 5.2 Neuron Models
  • 5.3 Network Architectures
  • 5.4 Perceptrons
  • 5.5 Linear Neuron and the Widrow-Hoff Learning Rule
  • 5.6 The Error-Correction Delta Rule
  • 5.7 Multi-Layer Perceptron (MLP) Networks and the Error-Backpropagation Algorithm
  • 5.8 Multi-Class Discrimination with MLP Networks
  • 5.9 Radial Basis Functions (RBF) Networks
  • 5.10 Genetic-Neural Systems
  • 6. Fuzzy Inference Systems
  • 6.1 Introduction
  • 6.2 Cognitive Uncertainty and Fuzzy Rule-Base
  • 6.3 Fuzzy Quantification of Knowledge
  • 6.4 Fuzzy Rule-Base and Approximate Reasoning
  • 6.5 Mamdani Model for Fuzzy Inference Systems
  • 6.6 Takagi-Sugeno Fuzzy Model
  • 6.7 Neuro-Fuzzy Inference Systems
  • 6.8 Gentic-Fuzzy Systems
  • 7. Data Clustering and Data Transformations
  • 7.1 Unsupervised Learning
  • 7.2 Engineering the Data
  • 7.3 Overview of Basic Clustering Methods
  • 7.4 K-Means Clustering
  • 7.5 Fuzzy K-Means Clustering
  • 7.6 Expectation-Maximization (EM) Algorithm and Gaussian Mixtures Clustering
  • 7.7 Some Useful Data Transformations
  • 7.8 Entropy-Based Method for Attribute Discretization
  • 7.9 Principal Components Analysis (PCA) for Attribute Reduction
  • 7.10 Rough Sets-Based Methods for Attribute Reduction
  • 8. Decision Tree Learning
  • 8.1 Introduction
  • 8.2 Example of a Classification Decision Tree
  • 8.3 Measures of Impurity for Evaluating Splits in Decision Trees
  • 8.4 ID3, C4.5, and CART Decision Trees
  • 8.5 Pruning the Tree
  • 8.6 Strengths and Weaknesses of Decision-Tree Approach
  • 8.7 Fuzzy Decision Trees
  • 9. Business Intelligence and Data Mining: Techniques and Applications
  • 9.1 An Introduction to Analytics
  • 9.2 The CRISP-DM (Cross Industry Standard Process for Data Mining) Model
  • 9.3 Data Warehousing and Online Analytical Processing
  • 9.4 Mining Frequent Patterns and Association Rules
  • 9.5 Intelligent Information Retrieval Systems
  • 9.6 Applications and Trends
  • 9.7 Technologies for Big Data
  • Appendix A Genetic Algorithm (GA) For Search Optimization
  • A.1 A Simple Overview of Genetics
  • A.2 Genetics on Computers
  • A.3 The Basic Genetic Algorithm
  • A.4 Beyond the Basic Genetic Algorithm
  • Appendix B Reinforcement Learning (RL)
  • B.1 Introduction
  • B.2 Elements of Reinforcement Learning
  • B.3 Basics of Dynamic Programming
  • B.4 Temporal Difference Learning
  • Datasets from Real-Life Applications for Machine Learning Experiments
  • Problems
  • References
  • Index.

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