1. INTRODUCTION
Who will benefit from this book
Overview of a Data Analytics Pipeline
Topics in a Nutshell
2. ABSTRACTION
Regression & tree models
Overview
Regression Models
Tree Models
Remarks
Exercises
3. RECOGNITION
Logistic regression & ranking
Overview
Logistic Regression Model
A Ranking Problem by Pairwise Comparison
Statistical Process Control using Decision Tree
Remarks
Exercise
4. RESONANCE
Bootstrap & random forests
Overview
How Bootstrap Works
Random Forests
Remarks
Exercises
5. LEARNING (I)
Cross validation & OOB
Overview
Cross-Validation
Out-of-bag error in Random Forest
Remarks
Exercises
6. DIAGNOSIS
Residuals & heterogeneity
Overview
Diagnosis in Regression
Diagnosis in Random Forests
Clustering
Remarks
Exercises
7. LEARNING (II)
SVM & ensemble Learning
Overview
Support Vector Machine
Ensemble Learning
Remarks
Exercises
data analytics
8. SCALABILITY
LASSO & PCA
Overview
LASSO
Principal Component Analysis
Remarks
Exercises
9. PRAGMATISM
Experience & experimental
Overview
Kernel Regression Model
Conditional Variance Regression Model
Remarks
Exercises
10. SYNTHESIS
Architecture & pipeline
Overview
Deep Learning
inTrees
Remarks
Exercises
CONCLUSION
APPENDIX: A BRIEF REVIEW OF BACKGROUND KNOWLEDGE
The normal distribution
Matrix operations
Optimization
Biography
Shuai Huang is an associate professor at the department of industrial & systems engineering at the university of Washington. He conducts interdisciplinary research in machine learning, data analytics, and applied operations research with applications on healthcare, manufacturing, and transportation areas.
Houtao Deng is a data science researcher and practitioner. He developed several new decision tree methods such as inTrees. He has built data-driven products for forecasting, scheduling, pricing, recommendation, fraud detection, and image recognition.
"Another strength of the book is that the authors cover the regression methods comprehensively, starting from the relationship between variables, to the connections between methods. As a result, this book may be an introductory guide for health care professionals, students, and lecturers, both by showing the exercises with manual solutions and giving the R coding of the methods."
-Selen Yilmaz Isikhan in ISCB, September 2022
