Shukla Dr. Brahma Datta - Machine Learning & Genetic Algorithms

Machine Learning & Genetic Algorithms

Preface

Machine learning is a computer programming technique in which software is built in such a way that it can learn new facts from itself and make decisions on its own when necessary.

Machine learning (ML) is a large discipline, and this book covers a lot of ground. We attempted to cover all aspects of the subject. This book is designed for students enrolled in MCA, M.Sc. CS/IT, M.S., M. Tech, B.Tech , B.E. CS/IT, B.Sc. CS and BCA programs.

The most notable characteristic of this book is that each chapter is taught in a straightforward manner so that the student can grasp the subject. Due to space limits, we have included a number of related ideas and tasks for the benefit of both students and teachers.

Despite our best efforts, there may be some inaccuracies. If some readers bring these to my attention, we will be grateful. Suggestions and criticisms for the book's enhancement would be warmly received.

Dr. Brahma Datta Shukla (Ph.D., M.Tech., MCA) &

Ms. Pragya Singh Tomar (Ph.D.(p), M.Phil., MCA)

Institute of Computer Science,

Vikram University,

Ujjain (M.P.)

Acknowledgments

To begin, we would want to express our gratitude to the all-knowing, all-powerful almighty, whose blessings have been extremely beneficial to us throughout this work.

Honorable Dr. Umesh Kumar Singh, Director & Head, Institute of Computer Science, Vikram University, Ujjain (M.P.) owes us his undying gratitude and special thanks for his wise counsel. It is thanks to inspiration, active assistance, and continuous encouragement at every stage of our work that we are able to exhibit our work in this format.

Honorable Prof. Akhilesh Kumar Pandey, Vice Chancellor, Vikram University, Ujjain (MP), whose eager assistance and good advice were always available to us, deserves our heartfelt gratitude.

We owe a huge debt of gratitude. Mrs. Asha Tomar and Mr. Rajendra Singh Tomar (parents of Ms. Pragya Singh Tomar) and Mrs. Ramkeshni Shukla and Aacharya Rajkishore Shukla (parents of Dr. Brahma Datta Shukla) who have always rewarded us with laurels and weathered our ups and downs with wonderful patience and good grace. We heartily thanked Mr. Swadheen Singh Tomar (brother) for his constant and outspoken encroachment on the completion of this work. We are also thanked our friend Mrs. Priya Nigam for her moral support.

Finally, I want to convey my heartfelt gratitude to Dr. Anurag Shrivastava, Principal and Professor (ECE), Lakshmi Narain College of Technology and Science Indore, MP, India, for his significant support in publishing this work.

Dr. Brahma Datta Shukla (Ph.D., M.Tech., MCA) &

Ms. Pragya Singh Tomar (Ph.D.(p), M.Phil., MCA)

Institute of Computer Science,

Vikram University,

Ujjain (M.P.)

Machine Learning

Chapter 1: Introduction to Machine Learning

Introduction ,Components of Learning , Learning Models , Geometric Models, Probabilistic Models, Logic Models, Grouping and Grading, Designing a Learning System, Types of Learning, Supervised, Unsupervised, Reinforcement, Perspectives and Issues, Version Spaces, PAC Learning, VC Dimension.

Chapter: 2 Supervised and Unsupervised Learning

Decision Trees: ID3, Classification and Regression Trees, Regression: Linear Regression, Multiple Linear Regression, Logistic Regression, Neural Networks: Introduction, Perception, Multilayer Perception, Support Vector Machines: Linear and Non-Linear, Kernel Functions, K Nearest Neighbors.

Introduction to clustering, K-means clustering, K-Mode Clustering.

Model Combination Schemes, Voting, Error-Correcting Output Codes, Bagging: Random Forest Trees, Boosting: Adaboost, Stacking.

Gaussian mixture models - The Expectation-Maximization (EM) Algorithm, Information Criteria, Nearest neighbour methods - Nearest Neighbour Smoothing, Efficient Distance Computations: the KD-Tree, Distance Measures.

Introduction, Learning Task, Q Learning, Non deterministic Rewards and actions, temporal-difference learning, Relationship to Dynamic Programming, Active reinforcement learning, Generalization in reinforcement learning.

Motivation, Basics of Sampling Theory: Error Estimation and Estimating Binomial Proportions, The Binomial Distribution, Estimators, Bias, and Variance

Chapter: 5 Genetic Algorithms

Motivation, Genetic Algorithms: Representing Hypotheses, Genetic Operator, Fitness Function and Selection, An Illustrative Example, Hypothesis Space Search, Genetic Programming, Models of Evolution and Learning: Lamarkian Evolution, Baldwin Effect, Parallelizing Genetic Algorithms.