Publish a Book Chapter in "Mathematics for Data Science and Engineering Applications (Volume - 1)"
Mathematics Edited Book | Edited Book on Mathematics
This edited book on mathematics titled "Mathematics for Data Science and Engineering Applications" mainly focuses on various topics such as linear algebra for data, matrix factorization methods, eigenvalues in applications etc., and the rest are given below in the Scope of the book. This mathematics edited book will be published with ISBN number after following a proper double blind peer reviewed process. All the chapters of this mathematics edited book will be published in a very illustrative manner for easy reader understanding.
Author can download mathematics edited books authorship responsibility and copyright form: Click Here
Book Scope
- Linear Algebra for Data
- Matrix Factorization Methods
- Eigenvalues in Applications
- Singular Value Decomposition
- Vector Spaces in ML
- Orthogonality and Projections
- Least Squares Regression
- Numerical Linear Algebra
- Sparse Matrix Techniques
- Low-Rank Approximations
- Probability Foundations for DS
- Random Variables and Distributions
- Conditional Probability Models
- Bayesian Inference Basics
- Maximum Likelihood Estimation
- Hypothesis Testing Methods
- Confidence Interval Estimation
- Sampling and Resampling
- Monte Carlo Methods
- Markov Chains in Data
- Calculus for Optimization
- Multivariable Calculus Tools
- Gradients and Jacobians
- Hessians and Curvature
- Constrained Optimization Methods
- Lagrange Multipliers Applications
- Convex Sets and Functions
- Gradient Descent Variants
- Stochastic Gradient Methods
- Regularization Mathematics
- Differential Equations in Engineering
- Numerical ODE Solvers
- Numerical PDE Applications
- Fourier Series Applications
- Fourier Transform in Signals
- Laplace Transform Methods
- Wavelets in Data Analysis
- Signal Processing Mathematics
- Filtering and Smoothing
- Time Series Decomposition
- Graph Theory for Networks
- Network Centrality Measures
- Spectral Graph Methods
- Random Graph Models
- Optimization on Graphs
- Clustering and Community Detection
- Information Theory Measures
- Entropy and Mutual Information
- Statistical Learning Theory
- Mathematical Ethics in AI
Author Guidelines
To download guidelines: Click here
Deadline
31 Jan 2026
Submit Chapter
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OR
Send your chapter on helmandbooks@gmail.com
