Chapter 1 Introduction to Machine Learning
1.1 Definition of Machine Learning
1.2 History of Machine Learning
1.2.1 Artificial Intelligence, Machine Learning, and Deep Learning
1.2.2 Fields Related to Machine Learning
1.3 Workflow of Machine Learning
1.4 Types of Machine Learning Algorithms
1.4.1 Supervised Learning
1.4.2 Unsupervised Learning
1.4.3 Semi-supervised Learning
1.4.4 Reinforced Learning
1.5 Organization of the Textbook
1.6 Summary
Chapter 2 Feature Engineering
2.1 Data Normalization
2.1.1 Min-max Normalization
2.1.2 Standard Normalization
2.2 Data Discretization
2.2.1 Binning
2.2.2 Clustering Analysis
2.2.3 Entropy-based Discretization
2.2.4 Correlation Analysis
2.3 Feature Selection
2.3.1 Filter Feature Selection
2.3.2 Wrapper Feature Selection
2.3.3 Embedded Methods
2.4 Feature Extraction
2.4.1 Principal Components Analysis
2.4.2 Linear Discriminant Analysis
2.4.3 Autoencoder
2.5 Summary
Chapter 3 Instance-Based Learning
3.1 Overview of IBL
3.2 Components of KNN
3.2.1 Measure the Similarity between Instances
3.2.2 How to Choose K
3.2.3 Assign the Class Label
3.2.4 Time Complexity
3.3 Variants of KNN
3.3.1 Attribute Weighted KNN
3.3.2 Distance Weighted KNN
3.4 Strengths and Weaknesses of KNN
Chapter 4 Decision Tree Learning
4.1 Decision Tree Representation
4.1.1 Component of Decision Tree
4.1.2 How to use Decision Trees for Classification?
4.1.3 How to Generate Rules from Decision Trees?
4.1.4 Popular Algorithms to Generate Decision Trees
4.2 ID3 Algorithm
4.2.1 Select the best Attribute
