Preface
Acknowledgements
List of Acronyms

Chapter 1: Introduction to Machine Learning
Evolution of Machine Learning | Paradigms for ML | Learning by Rote | Learning by Deduction | Learning by Abduction | Learning by Induction | Reinforcement Learning | Types of Data | Matching | Stages in Machine Learning | Data Acquisition | Feature Engineering | Data Representation | Model Selection | Model Learning | Model Evaluation | Model Prediction | Model Explanation | Search and Learning | Explanation Offered by the Model | Data Sets Used

Chapter 2: Nearest Neighbor-Based Models
Introduction to Proximity Measures | Distance Measures | Minkowski Distance |Weighted Distance Measure | Non-Metric Similarity Functions | Levenshtein Distance | Mutual Neighborhood Distance (MND) | Proximity Between Binary Patterns | Different Classification Algorithms Based on the Distance Measures | Nearest Neighbor Classifier (NNC) | K-Nearest Neighbor Classifier | Weighted K-Nearest Neighbor (WKNN) Algorithm | Radius Distance Nearest Neighbor Algorithm | Tree-Based Nearest Neighbor Algorithm | Branch and Bound Method | Leader Clustering | KNN Regression | Concentration Effect and Fractional Norms | Performance Measures | Performance of Classifiers | Performance of Regression Algorithms | Area Under the ROC Curve for the Breast Cancer Data Set

Chapter 3: Models Based on Decision Trees
Introduction to Decision Trees | Decision Trees for Classification | Impurity Measures for Decision Tree Construction | Properties of the Decision Tree Classifier (DTC) | Applications in Breast Cancer Data | Embedded Schemes for Feature Selection | Regression Based on Decision Trees | Bias–Variance Trade-off | Random Forests for Classification and Regression | Comparison of DT and RF Models on Olivetti Face Data | AdaBoost Classifier | Regression Using DT-Based Models | Gradient Boosting (GB) | Practical Application

Chapter 4: The Bayes Classifier
Introduction to the Bayes Classifier | Probability, Conditional Probability and Bayes’ Rule | Conditional Probability | Total Probability | Bayes’ Rule and Inference | Bayes’ Rule and Classification | Random Variables, Probability Mass Function, Probability Density Function and Cumulative Distribution Function, Expectation and Variance | Random Variables | Probability Mass Function (PMF) | Binomial Random Variable | Cumulative Distribution Function (CDF) | Continuous Random Variables | Expectation of a Random Variable | Variance of a Random Variable | Normal Distribution | The Bayes Classifier and its Optimality | Multi-Class Classification | Parametric and Non-Parametric Schemes for Density Estimation | Parametric Schemes | Class Conditional Independence and Na.ve Bayes Classifier | Estimation of the Probability Structure | Naive Bayes Classifier (NBC)

Chapter 5: Machine Learning Based on Frequent Itemsets
Introduction to the Frequent Itemset Approach | Frequent Itemsets | Frequent Itemset Generation | Frequent Itemset Generation Strategies | Apriori Algorithm | Frequent Pattern Tree and Variants | FP Tree-Based Frequent Itemset Generation | Pattern Count (PC) Tree-Based Frequent Itemset Generation | Frequent Itemset Generation Using the PC Tree | Dynamic Mining of Frequent Itemsets | Classification Rule Mining | Frequent Itemsets for Classification Using PC Tree | Frequent Itemsets for Clustering Using the PC Tree

Chapter 6: Representation
Introduction to Representation | Feature Selection | Linear Feature Extraction | Vector Spaces | Basis of a Vector Space | Row Vectors and Column Vectors | Linear Transformations | Eigenvalues and Eigenvectors | Symmetric Matrices | Rank of a Matrix | Principal Component Analysis | Experimental Results on Olivetti Face Data | Singular Value Decomposition | PCA and SVD | Random Projections

Chapter 7: Clustering
Introduction to Clustering | Partitioning of Data | Data Re-organization | Data Compression | Summarization | Matrix Factorization | Clustering of Patterns | Data Abstraction | Clustering Algorithms | Divisive Clustering | Agglomerative Clustering | Partitional Clustering | K-Means Clustering | K-Means++ Clustering | Soft Partitioning | Soft Clustering | Fuzzy C-Means Clustering | Rough Clustering | Rough K-Means Clustering Algorithm | Expectation Maximization-Based Clustering | Spectral Clustering | Clustering Large Data Sets | Divide-and-Conquer Method

Chapter 8: Linear Discriminants for Machine Learning
Introduction to Linear Discriminants | Linear Discriminants for Classification | Parameters Involved in the Linear Discriminant Function | Learning w and b | Perceptron Classifier | Perceptron Learning Algorithm | Convergence of the Learning Algorithm | Linearly Non-Separable Classes | Multi-Class Problems | Support Vector Machines | Linearly Non-Separable Case | Non-linear SVM | Kernel Trick | Logistic Regression | Linear Regression | Sigmoid Function | Learning w and b in Logistic Regression | Multi-Layer Perceptrons (MLPs) | Backpropagation for Training an MLP | Results on the Digits Data Set

Chapter 9: Deep Learning
Introduction to Deep Learning | Non-Linear Feature Extraction Using Autoencoders | Comparison on the Digits Data Set | Deep Neural Networks | Activation Functions | Initializing Weights | Improved Optimization Methods | Adaptive Optimization | Loss Functions | Regularization | Adding Noise to the Output or Label Smoothing | Experimental Results on the MNIST Data Set | Convolutional Neural Networks | Convolution | Padding Zero Rows and Columns | Pooling to Reduce Dimensionality | Recurrent Neural Networks | Training an RNN | Encoder–Decoder Models | Generative Adversarial Networks

Conclusions
Appendix – Hints to Practical Exercises
Index