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