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Books > Professional & Technical > Electronics & communications engineering > Electronics engineering > Automatic control engineering
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Artificial Intelligence and Causal Inference (Hardcover)
Loot Price: R3,210
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Artificial Intelligence and Causal Inference (Hardcover)
Series: Chapman & Hall/CRC Machine Learning & Pattern Recognition
Expected to ship within 12 - 17 working days
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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. Understanding, transfer and generalization are
major principles that give rise intelligence. One of a key
component for understanding is causal inference. Causal inference
includes intervention, domain shift learning, temporal structure
and counterfactual thinking as major concepts to understand
causation and reasoning. Unfortunately, these essential components
of the causality are often overlooked by machine learning, which
leads to some failure of the deep learning. AI and causal inference
involve (1) using AI techniques as major tools for causal analysis
and (2) applying the causal concepts and causal analysis methods to
solving AI problems. The purpose of this book is to fill the gap
between the AI and modern causal analysis for further facilitating
the AI revolution. This book is ideal for graduate students and
researchers in AI, data science, causal inference, statistics,
genomics, bioinformatics and precision medicine. Key Features:
Cover three types of neural networks, formulate deep learning as an
optimal control problem and use Pontryagin's Maximum Principle for
network training. Deep learning for nonlinear mediation and
instrumental variable causal analysis. Construction of causal
networks is formulated as a continuous optimization problem.
Transformer and attention are used to encode-decode graphics. RL is
used to infer large causal networks. Use VAE, GAN, neural
differential equations, recurrent neural network (RNN) and RL to
estimate counterfactual outcomes. AI-based methods for estimation
of individualized treatment effect in the presence of network
interference.
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