We have great pleasure in presenting First edition “Introduction to Machine Learning” written for students of UG courses. The related matters are written in a simple and easily understandable.
This volume is an attempt to provide the students with thorough understanding of Introduction to Machine Learning. We have presented the subject matter in a systematic manner with liberal use of charts and diagrams where ever necessary so as to make it interesting and sustain students’ interest. We thank almighty for showering his substantial blessings and giving us the determination in preparing this book.
Contents –
Unit – 1 Foundations for ML
Introduction
Foundations for ML
ML Techniques Overview
Validation Techniques (Cross-Validations)
Feature Reduction/Dimensionality Reduction
Principal Components Analysis
(Eigen Values, Eigen Vectors, Orthogonality)
Clustering:
Distance Measures
Different Clustering Methods (Distance, Density, Hierarchical)
Iterative Distance-based Clustering
Dealing with Continuous
Categorical Values in K-Means
Constructing a Hierarchical Cluster
K-Medoids
k-Mode
Density-based Clustering
Measures of Quality of Clustering
Review Questions
Unit – 2 Classification
Introduction
Classification
Naïve Bayes Classifier
Model Assumptions
Probability Estimation
Required Data Processing
M-estimates
Feature Selection
Mutual Information
Classifier
K-Nearest Neighbors
Computational Geometry
Voronoi Diagrams
Delaunay Triangulations
K Nearest Neighbor Algorithm
Wilson Editing and Triangulations
Aspects to Consider while Designing K-Nearest Neighbor
Review Questions
Unit – 3 Support Vector Machines
Introduction
Support Vector Machines
Linear Learning Machines
Kernel Space
Making Kernels
Working in Feature Space
SVM for Classification
Regression Problems
Decision Trees: ID4, C4.5, CART
Ensembles Methods
Bagging & Boosting and its Impact on Bias and Variance
C5.0 Boosting
Random Forest
Gradient Boosting Machines and XG Boost
Review Questions
Unit – 4 Association Rule Mining
Introduction
Association Rule Mining
The Applications of Association Rule Mining
Market Basket
Recommendation Engines, etc.
A mathematical Model for Association Analysis
Large item Sets
Association Rules, Apriori
Constructs Large item Sets with mini sup by Iterations
Interestingness of Discover Rules
Application Examples
Association Analysis vs. Classification
FP-trees
Review Questions
Lab
