Statistical Modeling in Machine Learning: Concepts and Applications

Why Statistical Modeling Still Drives Modern Machine Learning

Statistical modeling may not grab headlines the way deep neural networks do, but it remains the quiet backbone of many reliable machine learning systems. This edited collection, Statistical Modeling in Machine Learning: Concepts and Applications, gathers researchers and practitioners who show exactly why that foundation matters—from regression and classification to dictionary learning and uncertainty quantification.

Inside the Book

The volume balances conceptual depth with hands-on case studies. Early chapters introduce statistical approaches to machine learning and walk through a prison crowding prediction case study. You’ll then see how web scraping with Python can feed real datasets into analytical workflows, how machine learning techniques were applied to COVID-19 data, and how discriminative dictionary learning uses statistical methods to improve sparse representations.

Practical Concepts You Can Use

• Regression and classification fundamentals with real-world context
• Web scraping in Python for data collection
• Uncertainty quantification for computational fluid dynamics simulations
• Simple versus complex classification algorithm comparisons
• Medical data analysis with machine learning

Who Will Find This Useful

Researchers, data scientists, and graduate students who need a rigorous but readable bridge between statistical theory and applied machine learning will appreciate the mix of methodology and implementation. The chapters are structured to help you understand not just what works, but why statistical reasoning often determines whether a model generalizes well beyond a training set.

A Reference for Real Problems

Rather than celebrating novelty for its own sake, the contributors focus on techniques that have proven value in prediction, classification, and uncertainty-aware modeling. If you’re building models for complex data and want deeper insight into the statistical assumptions behind them, this book offers a practical, example-rich guide.