Synthetic Data and Generative AI

Vincent Granville

Jan 2024 · Elsevier

About this ebook

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.

• Emphasizes numerical stability and performance of algorithms (computational complexity)
• Focuses on explainable AI/interpretable machine learning, with heavy use of synthetic data and generative models, a new trend in the field
• Includes new, easier construction of confidence regions, without statistics, a simple alternative to the powerful, well-known XGBoost technique
• Covers automation of data cleaning, favoring easier solutions when possible
• Includes chapters dedicated fully to synthetic data applications: fractal-like terrain generation with the diamond-square algorithm, and synthetic star clusters evolving over time and bound by gravity

About the author

Dr. Vincent Granville is a pioneering data scientist and machine learning expert, co-founder of Data Science Central (acquired by TechTarget in 2020), founder of MLTechniques.com, former VC-funded executive, author, and patent owner. Dr. Granville’s past corporate experience includes Visa, Wells Fargo, eBay, NBC, Microsoft, and CNET. Dr. Granville is also a former post-doc at Cambridge University, and the National Institute of Statistical Sciences (NISS). Dr. Granville has published in Journal of Number Theory, Journal of the Royal Statistical Society, and IEEE Transactions on Pattern Analysis and Machine Intelligence, and he is the author of Developing Analytic Talent: Becoming a Data Scientist, Wiley. Dr. Granville lives in Washington state, and enjoys doing research on stochastic processes, dynamical systems, experimental math, and probabilistic number theory. He has been listed in the Forbes magazine Top 20 Big Data Influencers.