Artificial Intelligence and Causal Inference

ISBN-13: 9780367859404 / Angielski / Twarda / 2022 / 368 str.

Momiao Xiong

Opis

Artificial Intelligence and Causal Inference address the recent development of  relationships between  artificial intelligence (AI) and causal inference. Despite significant progress in AI, a great challenge in AI development we are still facing is to understand mechanism underlying intelligence, including reasoning, planning and imagination. 

Recenzje

" 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.

Spis treści

1. Deep Neural Networks. 2. Deep Wide Neural Networks. 3. Dynamics of Output of Neural Networks. 4. Deep Generative Models. 5. Representation Learning. 5. Graph Representation Learning. 6. Deep Learning for Causal Inference. 7. Deep Learning for Counterfactual Inference and Treatment Estimation. 8. Reinforcement Learning, Meta-Learning for Causal Inference and Quantum Causal Analysis.

O autorze

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.