Chapter 1 Deep Neural Networks
Chapter 2 Gaussian Processes and Learning Dynamic for Wide Neural Networks
Chapter 3 Deep Generative Models
Chapter 4 Generative Adversarial Networks
Chapter 5 Deep Learning For Causal Inference
Chapter 6 Causal Inference in Time Series
Chapter 7 Deep Learning for Counterfactual Inference and Treatment Effect Estimation
Chapter 8 Reinforcement Learning and Causal
Biography
Momiao Xiong, is a professor in the Department of Biostatistics and Data Science, University of Texas School of Public Health, and a regular member in the Genetics & Epigenetics (G&E) Graduate Program at The University of Texas MD Anderson Cancer Center, UTHealth Graduate School of Biomedical Science. His interests are artificial intelligence, causal inference, bioinformatics and genomics.
" Both deep learning and causal inference are fast-moving fields, and the author covers the latest topics and methods well. The book has a high ratio of equations to text, and even more technical material is contained in appendices at the end of each chapter."
Stanley E. Lazic, University of Ottawa, Series A: Statisics in Society, 2022.
"The book is suitable for use in a graduate-level course on AI. The exercises are challenging but their answers are provided in the end of the book. Not all contents are understandable by the statistics community or commonly useful in the practice of statistics. I enjoyed reading this book. I recommend this book to engineering, data science, predictive business, statistics and computing professionals."
Ramalingam Shanmugam, School of Health Administration, Texas State University, San Marcos, Texas, Journal of Statistical Computation and Simulation, 2023.
