1. Introduction
1.1 Towards Intelligent Machines
1.2 Well-Posed Machine Learning Problems
1.3 Examples of Application 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 Bacic Linear Algebra in Machine Learning Techniques
1.10 Relevant Resources for Machine Learning

2. Supervised Learnig: 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 Induvtive 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 Mavhine 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 Reggression 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
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 Fearure 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
5.8 Multi-Class Discrimination with MLP Networks
5.9 Radial Basis Functions (RBF) Network
5.10 Genetic-Neural Systems

6. Fuzzy Inference System
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 Component 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 Appoach
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 snd Trends
9.7 Technologies dor Big Data

Appendix A Genetic Algorithm (GA) For Search Optimization
Appendix B Reinforcement Learning (RL)
Datasets form Real-Life Applications for Mavhine Kearning Experiments
Problems
References
Index