Synthetic Data and Generative AI covers the foundations of machine learning, with modern approaches to solving complex problems and the systematic generation and use of synthetic data. Emphasis is on scalability, automation, testing, optimizing, and interpretability (explainable AI). For instance, regression techniques – including logistic and Lasso – are presented as a single method, without using advanced linear algebra. Confidence regions and prediction intervals are built using parametric bootstrap, without statistical models or probability distributions. Models (including generative models and mixtures) are mostly used to create rich synthetic data to test and benchmark various methods.
1. Chapter 3 : Gentle introduction to linear algebra – synthetic time series - p. Page 47 2. Chapter 4 : Image and video generation: The art of visualizing high-dimensional data - p. Page 60 3. Chapter 5 : Synthetic clusters and alternative to GMM: Fast classification and clustering via image convolution filters - p. Page 82 4. Chapter 6 : Shape classification and synthetization via explainable AI - p. Page 102 5. Chapter 7 : Synthetic data, interpretable regression, and submodels: Little known secrets about linear regression - p. Page 111 6. Chapter 8 : From interpolation to fuzzy regression - p. Page 128 7. Chapter 9 : New interpolation methods for synthetization and prediction - p. Page 145 8. Chapter 10 : Synthetic tabular data: copulas vs enhanced GANs - p. Page 168 9. Chapter 11 : High quality random numbers for data synthetization - p. Page 200 10. Chapter 12 : Some unusual random walks: Testing and leveraging quasirandomness - p. Page 227 11. Chapter 13 : Divergent optimization algorithm and synthetic functions: When all else fails - p. Page 240 12. Chapter 14 : Synthetic terrain generation and AI-generated art - p. Page 258 13. Chapter 15 : Synthetic star cluster generation with collision graphs - p. Page 276 14. Chapter 16 : Perturbed lattice point process: alternative to GMM: Inference, nearest neighbor graph - p. Page 292 15. Chapter 17 : Synthetizing multiplicative functions in number theory: New perspective on the Riemann Hypothesis - p. Page 325 16. Chapter 18 : Text, sound generation, and other topics - p. Page 359 17. Glossary - p. Page 385 18. Index - p. Page 389
Synthetic Data and Generative AI covers the foundations of machine learning, with modern approaches to solving complex problems and the systematic generation and use of synthetic data. Emphasis is on scalability, automation, testing, optimizing, and interpretability (explainable AI). For instance, regression techniques – including logistic and Lasso – are presented as a single method, without using advanced linear algebra. Confidence regions and prediction intervals are built using parametric bootstrap, without statistical models or probability distributions. Models (including generative models and mixtures) are mostly used to create rich synthetic data to test and benchmark various methods.
1. Chapter 3 : Gentle introduction to linear algebra – synthetic time series - p. Page 47 2. Chapter 4 : Image and video generation: The art of visualizing high-dimensional data - p. Page 60 3. Chapter 5 : Synthetic clusters and alternative to GMM: Fast classification and clustering via image convolution filters - p. Page 82 4. Chapter 6 : Shape classification and synthetization via explainable AI - p. Page 102 5. Chapter 7 : Synthetic data, interpretable regression, and submodels: Little known secrets about linear regression - p. Page 111 6. Chapter 8 : From interpolation to fuzzy regression - p. Page 128 7. Chapter 9 : New interpolation methods for synthetization and prediction - p. Page 145 8. Chapter 10 : Synthetic tabular data: copulas vs enhanced GANs - p. Page 168 9. Chapter 11 : High quality random numbers for data synthetization - p. Page 200 10. Chapter 12 : Some unusual random walks: Testing and leveraging quasirandomness - p. Page 227 11. Chapter 13 : Divergent optimization algorithm and synthetic functions: When all else fails - p. Page 240 12. Chapter 14 : Synthetic terrain generation and AI-generated art - p. Page 258 13. Chapter 15 : Synthetic star cluster generation with collision graphs - p. Page 276 14. Chapter 16 : Perturbed lattice point process: alternative to GMM: Inference, nearest neighbor graph - p. Page 292 15. Chapter 17 : Synthetizing multiplicative functions in number theory: New perspective on the Riemann Hypothesis - p. Page 325 16. Chapter 18 : Text, sound generation, and other topics - p. Page 359 17. Glossary - p. Page 385 18. Index - p. Page 389
