This book unifies conventional statistical thinking and contemporary machine learning framework into a single overarching umbrella over data science. The book is designed to bridge the knowledge gap between conventional statistics and machine learning.
This book unifies conventional statistical thinking and contemporary machine learning framework into a single overarching umbrella over data science. The book is designed to bridge the knowledge gap between conventional statistics and machine learning.
John T. Chen is a professor of Statistics at Bowling Green State University. He completed his postdoctoral training at McMaster University (Canada) after earning a PhD degree in statistics at the University of Sydney (Australia). John has published research papers in statistics journals such as Biometrika as well as in medicine journals such as the Annals of Neurology. Clement Lee is a data scientist in a private firm in New York. He earned a Master's degree in applied mathematics from New York University, after graduating from Princeton University in computer science. Clement enjoys spending time with his beloved wife Belinda and their son Pascal. Lincy Y. Chen is a data scientist at JP Morgan Chase & Co. She graduated from Cornell University, winning the Edward M. Snyder Prize in Statistics. Lincy has published papers regarding refinements of machine learning methods.
Inhaltsangabe
Preface 1. Two Cultures in Data Science 2. Fundamental Instruments 3. Sensitivity and Specificity Trade-off 4. Bias and Variation Trade-off 5. Linear Prediction 6. Nonlinear Prediction 7. Minimum Risk Classification 8. Support Vectors and Duality Theorem 9. Decision Trees and Range Regressions 10. Unsupervised Learning and Optimization 11. Simultaneous Learning and Multiplicity Bibliography Index
Preface 1. Two Cultures in Data Science 2. Fundamental Instruments 3. Sensitivity and Specificity Trade-off 4. Bias and Variation Trade-off 5. Linear Prediction 6. Nonlinear Prediction 7. Minimum Risk Classification 8. Support Vectors and Duality Theorem 9. Decision Trees and Range Regressions 10. Unsupervised Learning and Optimization 11. Simultaneous Learning and Multiplicity Bibliography Index
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