Industrial AI

Jay Lee, Industrial AI: Applications with Sustainable Performance, Springer, 2020.

This book introduces Industrial AI in multiple dimensions. Industrial AI is a systematic discipline which focuses on developing, validating and deploying various machine learning algorithms for industrial applications with sustainable performance. Combined with the state-of-the-art sensing, communication and big data analytics platforms, a systematic Industrial AI methodology will allow integration of physical systems with computational models. The concept of Industrial AI is in infancy stage and may encompass the collective use of technologies such as Internet of Things, Cyber-Physical Systems and Big Data Analytics under the Industry 4.0 initiative where embedded computing devices, smart objects and the physical environment interact with each other to reach intended goals. A broad range of Industries including automotive, aerospace, healthcare, semiconductors, energy, transportation, mining, construction, and industrial automation could harness the power of Industrial AI to gain insights into the invisible relationship of the operation conditions and further use that insight to optimize their uptime, productivity and efficiency of their operations. In terms of predictive maintenance, Industrial AI can detect incipient changes in the system and predict the remains useful life and further to optimize maintenance tasks to avoid disruption to operations.

In Defense of a Liberal Education

Fareed Zakaria, In Defense of a Liberal Education, W. W. Norton & Company; 1 edition, March 28, 2016.

The liberal arts are under attack. The governors of Florida, Texas, and North Carolina have all pledged that they will not spend taxpayer money subsidizing the liberal arts, and they seem to have an unlikely ally in President Obama. While at a General Electric plant in early 2014, Obama remarked, "I promise you, folks can make a lot more, potentially, with skilled manufacturing or the trades than they might with an art history degree." These messages are hitting home: majors like English and history, once very popular and highly respected, are in steep decline. 

Markov-chain Monte Carlo for disease dynamics modelling

If you search "pandemic markov chain" under Google scholar, you will find many articles on this topic. For example, Introduction to particle Markov-chain Monte Carlo for disease dynamics modellers by A Endo, E van Leeuwen, M Baguelin - Epidemics, 2019 - Elsevier.

馬友友演奏巴哈大提琴組曲

Yo Yo Ma - Bach Six Cello Suites - BBC Proms 2015

「巴哈的大提琴組曲是我一直以來的音樂夥伴。在將近60年裡，給了我寄託、安慰和快樂，無論是在面對壓力、慶祝，抑或是失去的種種生命時刻。即使經過了三百年的今天，這種音樂仍具有這樣強大的力量、仍可以繼續幫助我們度過艱難的時期。與所有文化一樣，音樂可以幫助我們了解我們的環境、彼此以及我們自己。文化可以幫助我們想像美好的未來，將『他們』變成『我們』，使我們團結在一起。而這些事情在現今社會如此重要。」——馬友友

EE104/CME107 Introduction to Machine Learning

Sanjay Lall, EE104/CME107: Introduction to Machine Learning, Stanford University, Spring Quarter, 2021. (quarter: 10 weeks, Julia, YouTube)

Introduction to machine learning. Formulation of supervised and unsupervised learning problems. Regression and classification. Data standardization and feature engineering. Loss function selection and its effect on learning. Regularization and its role in controlling complexity. Validation and overfitting. Robustness to outliers. Simple numerical implementation. Experiments on data from a wide variety of engineering and other disciplines.

共同作者為 S. Boyd，兩位教授的寫作非常清楚，推薦。