Chiudi

Aggiungi l'articolo in

Chiudi
Aggiunto

L’articolo è stato aggiunto alla lista dei desideri

Chiudi

Crea nuova lista

Artificial Intelligence and Causal Inference - Momiao Xiong - cover
Artificial Intelligence and Causal Inference - Momiao Xiong - cover
Dati e Statistiche
Wishlist Salvato in 0 liste dei desideri
Artificial Intelligence and Causal Inference
Disponibile in 5 giorni lavorativi
66,21 €
-5% 69,69 €
66,21 € 69,69 € -5%
Disp. in 5 gg lavorativi
Chiudi
Altri venditori
Prezzo e spese di spedizione
ibs
66,21 € Spedizione gratuita
disponibile in 5 giorni lavorativi disponibile in 5 giorni lavorativi
Info
Nuovo
Altri venditori
Prezzo e spese di spedizione
ibs
66,21 € Spedizione gratuita
disponibile in 5 giorni lavorativi disponibile in 5 giorni lavorativi
Info
Nuovo
Altri venditori
Prezzo e spese di spedizione
Chiudi

Tutti i formati ed edizioni

Chiudi
Artificial Intelligence and Causal Inference - Momiao Xiong - cover
Chiudi

Promo attive (0)

Descrizione


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.
Leggi di più Leggi di meno

Dettagli

Chapman & Hall/CRC Machine Learning & Pattern Recognition
2024
Paperback / softback
368 p.
Testo in English
280 x 210 mm
730 gr.
9781032193281
Chiudi
Aggiunto

L'articolo è stato aggiunto al carrello

Chiudi

Aggiungi l'articolo in

Chiudi
Aggiunto

L’articolo è stato aggiunto alla lista dei desideri

Chiudi

Crea nuova lista

Chiudi

Chiudi

Siamo spiacenti si è verificato un errore imprevisto, la preghiamo di riprovare.

Chiudi

Verrai avvisato via email sulle novità di Nome Autore