| Description |
1 online resource |
| Contents |
Cover -- Title Page -- Copyright Page -- Contents -- Preface -- Section 1: Theoretical Fundamentals -- Chapter 1 Mathematical Foundation -- 1.1 Concept of Linear Algebra -- 1.1.1 Introduction -- 1.1.2 Vector Spaces -- 1.1.3 Linear Combination -- 1.1.4 Linearly Dependent and Independent Vectors -- 1.1.5 Linear Span, Basis and Subspace -- 1.1.6 Linear Transformation (or Linear Map) -- 1.1.7 Matrix Representation of Linear Transformation -- 1.1.7.1 Transformation Matrix -- 1.1.8 Range and Null Space of Linear Transformation -- 1.1.9 Invertible Linear Transformation |
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1.2 Eigenvalues, Eigenvectors, and Eigendecomposition of a Matrix -- 1.2.1 Characteristics Polynomial -- 1.2.1.1 Some Results on Eigenvalue -- 1.2.2 Eigendecomposition [11] -- 1.3 Introduction to Calculus -- 1.3.1 Function -- 1.3.2 Limits of Functions -- 1.3.2.1 Some Properties of Limits -- 1.3.2.2 1nfinite Limits -- 1.3.2.3 Limits at Infinity -- 1.3.3 Continuous Functions and Discontinuous Functions -- 1.3.3.1 Discontinuous Functions -- 1.3.3.2 Properties of Continuous Function -- 1.3.4 Differentiation -- References -- Chapter 2 Theory of Probability -- 2.1 Introduction -- 2.1.1 Definition |
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2.1.1.1 Statistical Definition of Probability -- 2.1.1.2 Mathematical Definition of Probability -- 2.1.2 Some Basic Terms of Probability -- 2.1.2.1 Trial and Event -- 2.1.2.2 Exhaustive Events (Exhaustive Cases) -- 2.1.2.3 Mutually Exclusive Events -- 2.1.2.4 Equally Likely Events -- 2.1.2.5 Certain Event or Sure Event -- 2.1.2.6 Impossible Event or Null Event (.) -- 2.1.2.7 Sample Space -- 2.1.2.8 Permutation and Combination -- 2.1.2.9 Examples -- 2.2 Independence in Probability -- 2.2.1 Independent Events -- 2.2.2 Examples: Solve the Following Problems -- 2.3 Conditional Probability |
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2.3.1 Definition -- 2.3.2 Mutually Independent Events -- 2.3.3 Examples -- 2.4 Cumulative Distribution Function -- 2.4.1 Properties -- 2.4.2 Example -- 2.5 Baye's Theorem -- 2.5.1 Theorem -- 2.5.1.1 Examples -- 2.6 Multivariate Gaussian Function -- 2.6.1 Definition -- 2.6.1.1 Univariate Gaussian (i.e., One Variable Gaussian) -- 2.6.1.2 Degenerate Univariate Gaussian -- 2.6.1.3 Multivariate Gaussian -- References -- Chapter 3 Correlation and Regression -- 3.1 Introduction -- 3.2 Correlation -- 3.2.1 Positive Correlation and Negative Correlation -- 3.2.2 Simple Correlation and Multiple Correlation |
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3.2.3 Partial Correlation and Total Correlation -- 3.2.4 Correlation Coefficient -- 3.3 Regression -- 3.3.1 Linear Regression -- 3.3.2 Logistic Regression -- 3.3.3 Polynomial Regression -- 3.3.4 Stepwise Regression -- 3.3.5 Ridge Regression -- 3.3.6 Lasso Regression -- 3.3.7 Elastic Net Regression -- 3.4 Conclusion -- References -- Section 2: Big Data and Pattern Recognition -- Chapter 4 Data Preprocess -- 4.1 Introduction -- 4.1.1 Need of Data Preprocessing -- 4.1.2 Main Tasks in Data Preprocessing -- 4.2 Data Cleaning -- 4.2.1 Missing Data -- 4.2.2 Noisy Data -- 4.3 Data Integration |
| Summary |
Including hands-on tools and numerous case studies, this book aims to provide awareness of algorithms used for machine learning and big data in the academic and professional community. -- Edited summary from book |
| Subject |
Big data.
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Disk access (Computer science)
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Big data
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Disk access (Computer science)
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| Form |
Electronic book
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| Author |
Ahmad, Khaleel, author
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Bin Ahmad, Khairol Amali, author
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| ISBN |
9781119654810 |
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1119654815 |
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1119654793 |
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9781119654797 |
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1523136960 |
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9781523136964 |
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1119654831 |
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9781119654834 |
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