: Ameet V Joshi
: Machine Learning and Artificial Intelligence
: Springer-Verlag
: 9783030266226
: 1
: CHF 76.40
:
: Elektronik, Elektrotechnik, Nachrichtentechnik
: English
: 262
: Wasserzeichen/DRM
: PC/MAC/eReader/Tablet
: PDF

This book provides comprehensive coverage of combined Artificial Intelligence (AI) and Machine Learning (ML) theory and applications. Rather than looking at the field from only a theoretical or only a practical perspective, this book unifies both perspectives to give holistic understanding. The first part introduces the concepts of AI and ML and their origin and current state. The second and third parts delve into conceptual and theoretic aspects of static and dynamic ML techniques. The forth part describes the practical applications where presented techniques can be applied. The fifth part introduces the user to some of the implementation strategies for solving real life ML problems. 

The book is appropriate for students in graduate and upper undergraduate courses in addition to researchers and professionals. It makes minimal use of mathematics to make the topics more intuitive and accessible.

  • Provides a guide to AI and ML with minimal use of mathematics to make the topics more intuitive and accessible;
  • Connects all ML and AI techniques to applications and introduces implementations.

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Dr. Ameet Joshi received his PhD from Michigan State University in 2006. He has over 15 years of experience in developing machine learning algorithms in various different industrial settings including Pipeline Inspection, Home Energy Disaggregation, Microsoft Cortana Intelligence and Business Intelligence in CRM. He is currently a Data Science Manager at Microsoft. Previously, he has worked as Machine Learning Specialist at Belkin International and a Director of Research at Microline Technology Corp. He is a member of several technical committees, has published in numerous conference and journal publications and contributed to edited books. He also has two patents and have received several industry awards including and Senior Membership of IEEE (which only 8% of members achieve).  

Foreword6
Preface9
Acknowledgments11
Contents12
Part I Introduction20
1 Introduction to AI and ML21
1.1 Introduction21
1.2 What Is AI22
1.3 What Is ML22
1.4 Organization of the Book23
1.4.1 Introduction23
1.4.2 Machine Learning23
1.4.3 Building End to End Pipelines24
1.4.4 Artificial Intelligence24
1.4.5 Implementations24
1.4.6 Conclusion25
2 Essential Concepts in Artificial Intelligence and Machine Learning26
2.1 Introduction26
2.2 Big Data and Not-So-Big Data26
2.2.1 What Is Big Data26
2.2.2 Why Should We Treat Big Data Differently?27
2.3 Types of Learning27
2.3.1 Supervised Learning27
2.3.2 Unsupervised Learning28
2.3.3 Reinforcement Learning28
2.4 Machine Learning Methods Based on Time28
2.4.1 Static Learning28
2.4.2 Dynamic Learning29
2.5 Dimensionality29
2.5.1 Curse of Dimensionality30
2.6 Linearity and Nonlinearity30
2.7 Occam's Razor35
2.8 No Free Lunch Theorem35
2.9 Law of Diminishing Returns36
2.10 Early Trends in Machine Learning36
2.10.1 Expert Systems36
2.11 Conclusion37
3 Data Understanding, Representation, and Visualization38
3.1 Introduction38
3.2 Understanding the Data38
3.2.1 Understanding Entities39
3.2.2 Understanding Attributes39
3.2.3 Understanding Data Types41
3.3 Representation and Visualization of the Data41
3.3.1 Principal Component Analysis41
3.3.2 Linear Discriminant Analysis44
3.4 Conclusion46
Part II Machine Learning47
4 Linear Methods48
4.1 Introduction48
4.2 Linear and Generalized Linear Models49
4.3 Linear Regression49
4.3.1 Defining the Problem49
4.3.2 Solving the Problem50
4.4 Regularized Linear Regression51
4.4.1 Regularization51
4.4.2 Ridge Regression51
4.4.3 Lasso Regression52
4.5 Generalized Linear Models (GLM)52
4.5.1 Logistic Regression52
4.6 k-Nearest Neighbor (KNN) Algorithm53
4.6.1 Definition of KNN53
4.6.2 Classification and Regression55
4.6.3 Other Variations of KNN55
4.7 Conclusion56
5 Perceptron and Neural Networks57
5.1 Introduction57
5.2 Perceptron57
5.3 Multilayered Perceptron or Artificial Neural Network58
5.3.1 Feedforward Operation58
5.3.2 Nonlinear MLP or Nonlinear ANN59
5.3.2.1 Activation Functions59
5.3.3 Training MLP59
5.3.3.1 Online or Stochastic Learning61
5.3.3.2 Batch Learning61
5.3.4 Hidden Layers62
5.4 Radial Basis Function Networks62
5.4.1 Interpretation of RBF Networks63
5.5 Overfitting and Regularization64
5.5.1 L1 and L2 Regularization64
5.5.2 Dropout Regularization65
5.6 Conclusion65
6 Decision Trees66
6.1 Introduction66
6.2 Why Decision Trees?67
6.2.1 Types of Decision Trees67
6.3 Algorithms for Building Decision Trees67
6.4 Regression Tree68
6.5 Classification Tree70
6.6 Decision Metrics70
6.6.1 Misclassification Error70
6.6.2 Gini Index70
6.6.3 Cross-Entropy or Deviance71
6.7 CHAID71
6.7.1 CHAID Algorithm72
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