Mathematics Edited Book | Edited Book on Business and Management
This edited book on mathematics titled "Mathematical Foundations for Data Science and Engineering Applications" mainly focuses on various topics such as linear algebra basics, matrix factorization, vector spaces etc., and the rest are given below in the Scope of the book. This mathematics edited book will be published with ISBN numbers after following a proper double blind peer reviewed process. All the chapters of this mathematics edited book will be published in a proper style, so that reader can easily understand and learn.
Author can download this mathematics edited book titled "Mathematical Foundations for Data Science and Engineering Applications" authorship responsibility and copyright form:
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 978-3-96492-716-3Invited Topics
- Linear Algebra as a Language for Data: Vectors, Bases, and
Subspaces
- Matrix Decompositions in Data Science: QR, LU, and SVD in
Practice
- Eigenvalues in Engineering Systems: Stability, Resonance,
and Modes
- Numerical Linear Algebra for Large-Scale Learning:
Conditioning and Complexity
- Sparse Matrices and Structured Computations in Scientific
Computing
- Probability Foundations for Modeling Uncertainty in
Engineering Data
- Random Variables and Transformations: Tools for Feature
Engineering
- Distribution Theory for Data Science: Families, Estimation,
and Fit
- Bayesian Reasoning in Engineering Decisions: Priors,
Posteriors, and Prediction
- Likelihood, Estimation, and Identifiability: Mathematical
Perspectives
- Statistical Inference Under Model Misspecification:
Robustness and Limits
- Confidence Intervals and Uncertainty Quantification for
Predictive Models
- Hypothesis Testing in High Dimensions: Pitfalls and Modern
Methods
- Regression as Projection: Geometry and Interpretation of
Least Squares
- Regularization Theory: Ridge, Lasso, and Elastic Net from First
Principles
- Bias–Variance Trade-Off: Mathematical Derivations and
Practical Implications
- Convex Sets and Convex Functions: Foundations for
Optimization in ML
- Optimality Conditions: Gradients, Subgradients, and KKT
Theory
- Gradient Descent Methods: Convergence Rates and Practical
Variants
- Stochastic Gradient Methods: Noise, Stability, and
Generalization
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ISBN
ISBN: 978-3-96492-716-3
Book Scope
- Linear Algebra Basics
- Matrix Factorization
- Vector Spaces
- Eigenvalues and Eigenvectors
- Singular Value Decomposition
- Numerical Linear Algebra
- Probability Axioms
- Random Variables
- Common Distributions
- Bayesian Basics
- Statistical Inference
- Confidence Intervals
- Hypothesis Testing
- Regression Basics
- Regularization Methods
- Convex Optimization
- Gradient Descent
- Stochastic Optimization
- Lagrange Multipliers
- Duality Theory
- Multivariate Calculus
- Differential Equations
- Fourier Series
- Fourier Transform
- Laplace Transform
- Discrete-Time Signals
- Sampling Theory
- Information Theory
- Entropy and Mutual Information
- Markov Chains
- Stochastic Processes
- Gaussian Processes
- Kernel Methods
- Reproducing Kernel Hilbert Spaces
- PCA and Dimensionality Reduction
- Manifold Learning
- Graph Theory Basics
- Spectral Graph Theory
- Random Graphs
- Numerical Optimization in ML
- Numerical Stability
- Error Analysis
- Interpolation Methods
- Numerical Integration
- Finite Difference Methods
- Finite Element Methods
- Control Systems Basics
- State-Space Models
- Queuing Theory Basics
- Engineering Reliability Models
Author Guidelines
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Submit Chapter
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Send your chapter on weserbooks@gmail.com
Deadline
31th Jan 2026
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