Table of Contents

Chapter 1: Bayesian Optimization Overview.- Chapter 2:  Gaussian Process.- Chapter 3: Bayesian Decision Theory and Expected Improvement.- Chapter 4 : Gaussian Process Regression with GPyTorch.- Chapter 5:  Monte Carlo Acquisition Function with Sobol Sequences and Random Restart.- Chapter 6 : Knowledge Gradient: Nested Optimization versus One-shot Learning.- Chapter 7 : Case Study: Tuning CNN Learning Rate with BoTorch.

About the Author

Peng Liu is an assistant professor of quantitative finance (practice) at Singapore Management University and an adjunct researcher at the National University of Singapore. He holds a Ph.D. in statistics from the National University of Singapore and has ten years of working experience as a data scientist across the banking, technology, and hospitality industries