Introduction to Machine Learning fourth edition 4th edition by Ethem Alpaydin – Ebook PDF Instant Download/Delivery: 0262043793, 978-0262043793
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ISBN 10: 0262043793
ISBN 13: 978-0262043793
Author: Ethem Alpaydin
A substantially revised fourth edition of a comprehensive textbook, including new coverage of recent advances in deep learning and neural networks.
The goal of machine learning is to program computers to use example data or past experience to solve a given problem. Machine learning underlies such exciting new technologies as self-driving cars, speech recognition, and translation applications. This substantially revised fourth edition of a comprehensive, widely used machine learning textbook offers new coverage of recent advances in the field in both theory and practice, including developments in deep learning and neural networks.
The book covers a broad array of topics not usually included in introductory machine learning texts, including supervised learning, Bayesian decision theory, parametric methods, semiparametric methods, nonparametric methods, multivariate analysis, hidden Markov models, reinforcement learning, kernel machines, graphical models, Bayesian estimation, and statistical testing. The fourth edition offers a new chapter on deep learning that discusses training, regularizing, and structuring deep neural networks such as convolutional and generative adversarial networks; new material in the chapter on reinforcement learning that covers the use of deep networks, the policy gradient methods, and deep reinforcement learning; new material in the chapter on multilayer perceptrons on autoencoders and the word2vec network; and discussion of a popular method of dimensionality reduction, t-SNE. New appendixes offer background material on linear algebra and optimization. End-of-chapter exercises help readers to apply concepts learned. Introduction to Machine Learning can be used in courses for advanced undergraduate and graduate students and as a reference for professionals.
Introduction to Machine Learning fourth edition 4th Table of contents:
1. Introduction
1.1 What Is Machine Learning
1.2 Examples of Machine Learning Applications
1.2.1 Association Rules
1.2.2 Classification
1.2.3 Regression
1.2.4 Unsupervised Learning
1.2.5 Reinforcement Learning
1.3 History
1.4 Related Topics
1.4.1 High-Performance Computing
1.4.2 Data Privacy and Security
1.4.3 Model Interpretability and Trust
1.4.4 Data Science
1.5 Exercises
1.6 References
2. Supervised Learning
2.1 Learning a Class from Examples
2.2 Vapnik-Chervonenkis Dimension
2.3 Probably Approximately Correct Learning
2.4 Noise
2.5 Learning Multiple Classes
2.6 Regression
2.7 Model Selection and Generalization
2.8 Dimensions of a Supervised Machine Learning Algorithm
2.9 Notes
2.10 Exercises
2.11 References
3. Bayesian Decision Theory
3.1 Introduction
3.2 Classification
3.3 Losses and Risks
3.4 Discriminant Functions
3.5 Association Rules
3.6 Notes
3.7 Exercises
3.8 References
4. Parametric Methods
4.1 Introduction
4.2 Maximum Likelihood Estimation
4.2.1 Bernoulli Density
4.2.2 Multinomial Density
4.2.3 Gaussian (Normal) Density
4.3 Evaluating an Estimator: Bias and Variance
4.4 The Bayes’ Estimator
4.5 Parametric Classification
4.6 Regression
4.7 Tuning Model Complexity: Bias/Variance Dilemma
4.8 Model Selection Procedures
4.9 Notes
4.10 Exercises
4.11 References
5. Multivariate Methods
5.1 Multivariate Data
5.2 Parameter Estimation
5.3 Estimation of Missing Values
5.4 Multivariate Normal Distribution
5.5 Multivariate Classification
5.6 Tuning Complexity
5.7 Discrete Features
5.8 Multivariate Regression
5.9 Notes
5.10 Exercises
5.11 References
6. Dimensionality Reduction
6.1 Introduction
6.2 Subset Selection
6.3 Principal Component Analysis
6.4 Feature Embedding
6.5 Factor Analysis
6.6 Singular Value Decomposition and Matrix Factorization
6.7 Multidimensional Scaling
6.8 Linear Discriminant Analysis
6.9 Canonical Correlation Analysis
6.10 Isomap
6.11 Locally Linear Embedding
6.12 Laplacian Eigenmaps
6.13 t-Distributed Stochastic Neighbor Embedding
6.14 Notes
6.15 Exercises
6.16 References
7. Clustering
7.1 Introduction
7.2 Mixture Densities
7.3 k-Means Clustering
7.4 Expectation-Maximization Algorithm
7.5 Mixtures of Latent Variable Models
7.6 Supervised Learning after Clustering
7.7 Spectral Clustering
7.8 Hierarchical Clustering
7.9 Choosing the Number of Clusters
7.10 Notes
7.11 Exercises
7.12 References
8. Nonparametric Methods
8.1 Introduction
8.2 Nonparametric Density Estimation
8.2.1 Histogram Estimator
8.2.2 Kernel Estimator
8.2.3 k-Nearest Neighbor Estimator
8.3 Generalization to Multivariate Data
8.4 Nonparametric Classification
8.5 Condensed Nearest Neighbor
8.6 Distance-Based Classification
8.7 Outlier Detection
8.8 Nonparametric Regression: Smoothing Models
8.8.1 Running Mean Smoother
8.8.2 Kernel Smoother
8.8.3 Running Line Smoother
8.9 How to Choose the Smoothing Parameter
8.10 Notes
8.11 Exercises
8.12 References
9. Decision Trees
9.1 Introduction
9.2 Univariate Trees
9.2.1 Classification Trees
9.2.2 Regression Trees
9.3 Pruning
9.4 Rule Extraction from Trees
9.5 Learning Rules from Data
9.6 Multivariate Trees
9.7 Notes
9.8 Exercises
9.9 References
10. Linear Discrimination
10.1 Introduction
10.2 Generalizing the Linear Model
10.3 Geometry of the Linear Discriminant
10.3.1 Two Classes
10.3.2 Multiple Classes
10.4 Pairwise Separation
10.5 Parametric Discrimination Revisited
10.6 Gradient Descent
10.7 Logistic Discrimination
10.7.1 Two Classes
10.7.2 Multiple Classes
10.7.3 Multiple Labels
10.8 Learning to Rank
10.9 Notes
10.10 Exercises
10.11 References
11. Multilayer Perceptrons
11.1 Introduction
11.1.1 Understanding the Brain
11.1.2 Neural Networks as a Paradigm for Parallel Processing
11.2 The Perceptron
11.3 Training a Perceptron
11.4 Learning Boolean Functions
11.5 Multilayer Perceptrons
11.6 MLP as a Universal Approximator
11.7 Backpropagation Algorithm
11.7.1 Nonlinear Regression
11.7.2 Two-Class Discrimination
11.7.3 Multiclass Discrimination
11.7.4 Multilabel Discrimination
11.8 Overtraining
11.9 Learning Hidden Representations
11.10 Autoencoders
11.11 Word2vec Architecture
11.12 Notes
11.13 Exercises
11.14 References
12. Deep Learning
12.1 Introduction
12.2 How to Train Multiple Hidden Layers
12.2.1 Rectified Linear Unit
12.2.2 Initialization
12.2.3 Generalizing Backpropagation to Multiple Hidden Layers
12.3 Improving Training Convergence
12.3.1 Momentum
12.3.2 Adaptive Learning Factor
12.3.3 Batch Normalization
12.4 Regularization
12.4.1 Hints
12.4.2 Weight Decay
12.4.3 Dropout
12.5 Convolutional Layers
12.5.1 The Idea
12.5.2 Formalization
12.5.3 Examples: LeNet-5 and AlexNet
12.5.4 Extensions
12.5.5 Multimodal Deep Networks
12.6 Tuning the Network Structure
12.6.1 Structure and Hyperparameter Search
12.6.2 Skip Connections
12.6.3 Gating Units
12.7 Learning Sequences
12.7.1 Example Tasks
12.7.2 Time-Delay Neural Networks
12.7.3 Recurrent Networks
12.7.4 Long Short-Term Memory Unit
12.7.5 Gated Recurrent Unit
12.8 Generative Adversarial Network
12.9 Notes
12.10 Exercises
12.11 References
13. Local Models
13.1 Introduction
13.2 Competitive Learning
13.2.1 Online k-Means
13.2.2 Adaptive Resonance Theory
13.2.3 Self-Organizing Maps
13.3 Radial Basis Functions
13.4 Incorporating Rule-Based Knowledge
13.5 Normalized Basis Functions
13.6 Competitive Basis Functions
13.7 Learning Vector Quantization
13.8 The Mixture of Experts
13.8.1 Cooperative Experts
13.8.2 Competitive Experts
13.9 Hierarchical Mixture of Experts and Soft Decision Trees
13.10 Notes
13.11 Exercises
13.12 References
14. Kernel Machines
14.1 Introduction
14.2 Optimal Separating Hyperplane
14.3 The Nonseparable Case: Soft Margin Hyperplane
14.4 v-SVM
14.5 Kernel Trick
14.6 Vectorial Kernels
14.7 Defining Kernels
14.8 Multiple Kernel Learning
14.9 Multiclass Kernel Machines
14.10 Kernel Machines for Regression
14.11 Kernel Machines for Ranking
14.12 One-Class Kernel Machines
14.13 Large Margin Nearest Neighbor Classifier
14.14 Kernel Dimensionality Reduction
14.15 Notes
14.16 Exercises
14.17 References
15. Graphical Models
15.1 Introduction
15.2 Canonical Cases for Conditional Independence
15.3 Generative Models
15.4 d-Separation
15.5 Belief Propagation
15.5.1 Chains
15.5.2 Trees
15.5.3 Polytrees
15.5.4 Junction Trees
15.6 Undirected Graphs: Markov Random Fields
15.7 Learning the Structure of a Graphical Model
15.8 Influence Diagrams
15.9 Notes
15.10 Exercises
15.11 References
16. Hidden Markov Models
16.1 Introduction
16.2 Discrete Markov Processes
16.3 Hidden Markov Models
16.4 Three Basic Problems of HMMs
16.5 Evaluation Problem
16.6 Finding the State Sequence
16.7 Learning Model Parameters
16.8 Continuous Observations
16.9 The HMM as a Graphical Model
16.10 Model Selection in HMMs
16.11 Notes
16.12 Exercises
16.13 References
17. Bayesian Estimation
17.1 Introduction
17.2 Bayesian Estimation of the Parameters of a Discrete Distribution
17.2.1 K > 2 States: Dirichlet Distribution
17.2.2 K = 2 States: Beta Distribution
17.3 Bayesian Estimation of the Parameters of a Gaussian Distribution
17.3.1 Univariate Case: Unknown Mean, Known Variance
17.3.2 Univariate Case: Unknown Mean, Unknown Variance
17.3.3 Multivariate Case: Unknown Mean, Unknown Covariance
17.4 Bayesian Estimation of the Parameters of a Function
17.4.1 Regression
17.4.2 Regression with Prior on Noise Precision
17.4.3 The Use of Basis/Kernel Functions
17.4.4 Bayesian Classification
17.5 Choosing a Prior
17.6 Bayesian Model Comparison
17.7 Bayesian Estimation of a Mixture Model
17.8 Nonparametric Bayesian Modeling
17.9 Gaussian Processes
17.10 Dirichlet Processes and Chinese Restaurants
17.11 Latent Dirichlet Allocation
17.12 Beta Processes and Indian Buffets
17.13 Notes
17.14 Exercises
17.15 References
18. Combining Multiple Learners
18.1 Rationale
18.2 Generating Diverse Learners
18.3 Model Combination Schemes
18.4 Voting
18.5 Error-Correcting Output Codes
18.6 Bagging
18.7 Boosting
18.8 The Mixture of Experts Revisited
18.9 Stacked Generalization
18.10 Fine-Tuning an Ensemble
18.10.1 Choosing a Subset of the Ensemble
18.10.2 Constructing Metalearners
18.11 Cascading
18.12 Notes
18.13 Exercises
18.14 References
19. Reinforcement Learning
19.1 Introduction
19.2 Single State Case: K-Armed Bandit
19.3 Elements of Reinforcement Learning
19.4 Model-Based Learning
19.4.1 Value Iteration
19.4.2 Policy Iteration
19.5 Temporal Difference Learning
19.5.1 Exploration Strategies
19.5.2 Deterministic Rewards and Actions
19.5.3 Nondeterministic Rewards and Actions
19.5.4 Eligibility Traces
19.6 Generalization
19.7 Partially Observable States
19.7.1 The Setting
19.7.2 Example: The Tiger Problem
19.8 Deep Q Learning
19.9 Policy Gradients
19.10 Learning to Play Backgammon and Go
19.11 Notes
19.12 Exercises
19.13 References
20. Design and Analysis of Machine Learning Experiments
20.1 Introduction
20.2 Factors, Response, and Strategy of Experimentation
20.3 Response Surface Design
20.4 Randomization, Replication, and Blocking
20.5 Guidelines for Machine Learning Experiments
20.6 CrossValidation and Resampling Methods
20.6.1 K-Fold Cross-Validation
20.6.2 5 × 2 Cross-Validation
20.6.3 Bootstrapping
20.7 Measuring Classifier Performance
20.8 Interval Estimation
20.9 Hypothesis Testing
20.10 Assessing a Classification Algorithm’s Performance
20.10.1 Binomial Test
20.10.2 Approximate Normal Test
20.10.3 t Test
20.11 Comparing Two Classification Algorithms
20.11.1 McNemar’s Test
20.11.2 K-Fold Cross-Validated Paired t Test
20.11.3 5 × 2 cv Paired t Test
20.11.4 5 × 2 cv Paired F Test
20.12 Comparing Multiple Algorithms: Analysis of Variance
20.13 Comparison over Multiple Datasets
20.13.1 Comparing Two Algorithms
20.13.2 Multiple Algorithms
20.14 Multivariate Tests
20.14.1 Comparing Two Algorithms
20.14.2 Comparing Multiple Algorithms
20.15 Notes
20.16 Exercises
20.17 References
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Tags: Ethem Alpaydin, Machine Learning