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Machine Learning and Big Data

Release Date: 2020/09/01
ISBN: 9781119654742
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Currently many different application areas for Big Data (BD) and Machine Learning (ML) are being explored. These promising application areas for BD/ML are the social sites, search engines, multimedia sharing sites, various stock exchange sites, online gaming, online survey sites and various news sites, and so on.  To date, various use-cases for this application area are being researched and developed. Software applications are already being published and used in various settings from education and training to discover useful hidden patterns and other information like customer choices and market trends that can help organizations make more informed and customer-oriented business decisions.

Combining BD with ML will provide powerful, largely unexplored application areas that will revolutionize practice in Videos Surveillance, Social Media Services, Email Spam and Malware Filtering, Online Fraud Detection, and so on.  It is very important to continuously monitor and understand these effects from safety and societal point of view.

Hence, the main purpose of this book is for researchers, software developers and practitioners, academicians and students to showcase novel use-cases and applications, present empirical research results from user-centered qualitative and quantitative experiments of these new applications, and facilitate a discussion forum to explore the latest trends in big data and machine learning by providing algorithms which can be trained to perform interdisciplinary techniques such as statistics, linear algebra, and optimization and also create automated systems that can sift through large volumes of data at high speed to make predictions or decisions without human intervention

Table of Contents

  1. Cover
  2. Title Page
  3. Copyright Page
  4. Preface
  5. Section 1: THEORETICAL FUNDAMENTALS
    1. 1 Mathematical Foundation
    2. 1.1 Concept of Linear Algebra
    3. 1.2 Eigenvalues, Eigenvectors, and Eigendecomposition of a Matrix
    4. 1.3 Introduction to Calculus
    5. References
    6. 2 Theory of Probability
    7. 2.1 Introduction
    8. 2.2 Independence in Probability
    9. 2.3 Conditional Probability
    10. 2.4 Cumulative Distribution Function
    11. 2.5 Baye’s Theorem
    12. 2.6 Multivariate Gaussian Function
    13. References
    14. 3 Correlation and Regression
    15. 3.1 Introduction
    16. 3.2 Correlation
    17. 3.3 Regression
    18. 3.4 Conclusion
    19. References
  6. Section 2: BIG DATA AND PATTERN RECOGNITION
    1. 4 Data Preprocess
    2. 4.1 Introduction
    3. 4.2 Data Cleaning
    4. 4.3 Data Integration
    5. 4.4 Data Transformation
    6. 4.5 Data Reduction
    7. 4.6 Conclusion
    8. Acknowledgements
    9. References
    10. 5 Big Data
    11. 5.1 Introduction
    12. 5.2 Big Data Evaluation With Its Tools
    13. 5.3 Architecture of Big Data
    14. 5.4 Issues and Challenges
    15. 5.5 Big Data Analytics Tools
    16. 5.6 Big Data Use Cases
    17. 5.7 Where IoT Meets Big Data
    18. 5.8 Role of Machine Learning For Big Data and IoT
    19. 5.9 Conclusion
    20. References
    21. 6 Pattern Recognition Concepts
    22. 6.1 Classifier
    23. 6.2 Feature Processing
    24. 6.3 Clustering
    25. 6.4 Conclusion
    26. References
  7. Section 3: MACHINE LEARNING: ALGORITHMS & APPLICATIONS
    1. 7 Machine Learning
    2. 7.1 History and Purpose of Machine Learning
    3. 7.2 Concept of Well-Defined Learning Problem
    4. 7.3 General-to-Specific Ordering Over Hypotheses
    5. 7.4 Version Spaces and Candidate Elimination Algorithm
    6. 7.5 Concepts of Machine Learning Algorithm
    7. Conclusion
    8. References
    9. 8 Performance of Supervised Learning Algorithms on Multi-Variate Datasets
    10. 8.1 Introduction
    11. 8.2 Supervised Learning Algorithms
    12. 8.3 Classification
    13. 8.4 Neural Network
    14. 8.5 Comparisons and Discussions
    15. 8.6 Summary and Conclusion
    16. References
    17. 9 Unsupervised Learning
    18. 9.1 Introduction
    19. 9.2 Related Work
    20. 9.3 Unsupervised Learning Algorithms
    21. 9.4 Classification of Unsupervised Learning Algorithms
    22. 9.5 Unsupervised Learning Algorithms in ML
    23. 9.6 Summary and Conclusions
    24. References
    25. 10 Semi-Supervised Learning
    26. 10.1 Introduction
    27. 10.2 Training Models
    28. 10.3 Generative Models—Introduction
    29. 10.4 S3VMs
    30. 10.5 Graph-Based Algorithms
    31. 10.6 Multiview Learning
    32. 10.7 Conclusion
    33. References
    34. 11 Reinforcement Learning
    35. 11.1 Introduction: Reinforcement Learning
    36. 11.2 Model-Free RL
    37. 11.3 Model-Based RL
    38. 11.4 Conclusion
    39. References
    40. 12 Application of Big Data and Machine Learning
    41. 12.1 Introduction
    42. 12.2 Motivation
    43. 12.3 Related Work
    44. 12.4 Application of Big Data and ML
    45. 12.5 Issues and Challenges
    46. 12.6 Conclusion
    47. References
  8. Section 4: MACHINE LEARNING’S NEXT FRONTIER
    1. 13 Transfer Learning
    2. 13.1 Introduction
    3. 13.2 Traditional Learning vs. Transfer Learning
    4. 13.3 Key Takeaways: Functionality
    5. 13.4 Transfer Learning Methodologies
    6. 13.5 Inductive Transfer Learning
    7. 13.6 Unsupervised Transfer Learning
    8. 13.7 Transductive Transfer Learning
    9. 13.8 Categories in Transfer Learning
    10. 13.9 Instance Transfer
    11. 13.10 Feature Representation Transfer
    12. 13.11 Parameter Transfer
    13. 13.12 Relational Knowledge Transfer
    14. 13.13 Relationship With Deep Learning
    15. 13.14 Applications: Allied Classical Problems
    16. 13.15 Further Advancements and Conclusion
    17. References
  9. Section 5: HANDS-ON AND CASE STUDY
    1. 14 Hands on MAHOUT—Machine Learning Tool
    2. 14.1 Introduction to Mahout
    3. 14.2 Installation Steps of Apache Mahout Using Cloudera
    4. 14.3 Installation Steps of Apache Mahout Using Windows 10
    5. 14.4 Installation Steps of Apache Mahout Using Eclipse
    6. 14.5 Mahout Algorithms
    7. 14.6 Conclusion
    8. References
    9. 15 Hands-On H2O Machine Learning Tool
    10. 15.1 Introduction
    11. 15.2 Installation
    12. 15.3 Interfaces
    13. 15.4 Programming Fundamentals
    14. 15.5 Machine Learning in H2O
    15. 15.6 Applications of H2O
    16. 15.7 Conclusion
    17. References
    18. 16 Case Study: Intrusion Detection System Using Machine Learning
    19. 16.1 Introduction
    20. 16.2 System Design
    21. 16.3 Existing Proposals
    22. 16.4 Approaches Used in Designing the Scenario
    23. 16.5 Result Analysis
    24. 16.6 Conclusion
    25. References
    26. 17 Inclusion of Security Features for Implications of Electronic Governance Activities
    27. 17.1 Introduction
    28. 17.2 Objective of E-Governance
    29. 17.3 Role of Identity in E-Governance
    30. 17.4 Status of E-Governance in Other Countries
    31. 17.5 Pros and Cons of E-Governance
    32. 17.6 Challenges of E-Governance in Machine Learning
    33. 17.7 Conclusion
    34. References
  10. Index
  11. End User License Agreement
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