Introduction Scientific Machine Learning Engineer Students
暫譯: 工程學系學生的科學機器學習導論

Bilionis Ilias

Introduction Scientific Machine Learning Engineer Students

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商品描述

Introduction to Scientific Machine Learning for Engineering Students offers a first-principles introduction to scientific machine learning for advanced undergraduates, graduate students, and practicing engineers. Using probability theory as the language of uncertainty, it develops a unified framework for modeling, inference, and prediction in engineering systems.

The book moves from probability, Monte Carlo methods, model calibration, and Bayesian inference to supervised learning, unsupervised learning, state-space models, Kalman filtering, probabilistic programming, and automated Bayesian inference. Physics-informed machine learning is integrated throughout, emphasizing the combination of data and physical insight.

Implemented entirely in reproducible Jupyter notebooks using Python, the text helps readers understand the foundations of modern methods, build models that reflect physical and causal structure, and calibrate them rigorously with data.

商品描述(中文翻譯)

《工程學生的科學機器學習導論》以第一原理為基礎，為高年級大學生、研究生與在職工程師介紹科學機器學習（scientific machine learning）。本書以機率論（probability theory）作為描述不確定性的語言，建立一套統一的工程系統建模、推論與預測架構。

本書的內容涵蓋機率、Monte Carlo 方法、模型校準（model calibration）與 Bayesian inference，進而延伸至 supervised learning、unsupervised learning、state-space models、Kalman filtering、probabilistic programming，以及 automated Bayesian inference。全書貫穿 physics-informed machine learning，強調結合資料與物理洞察。

本書完全以 Python 撰寫，並透過可重現的 Jupyter notebooks 實作，協助讀者理解現代方法的基礎、建立能反映物理結構與因果結構的模型，並利用資料對模型進行嚴謹校準。

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