Rui Wang, Danielle Robinson, Christos Faloutsos, Yuyang Wang, and Rose Yu, AutoODE: Bridging physics-based and data-driven modeling for COVID-19 forecasting, NeurIPS 2020 Workshop on Machine Learning in Public Health. (best paper award at the NeurIPS Machine Learning in Public Health Workshop)
As COVID-19 continues to spread, accurately forecasting the number of newly infected, removed and death cases has become a crucial task in public health. While mechanics compartment models are widely-used in epidemic modeling, data-driven models are emerging for disease forecasting. In this work, we investigate these two types of methods for COVID-19 forecasting. Through a comprehensive study, we find that data-driven models outperform physics-based models on the number of death cases prediction. Meanwhile, physics-based models have superior performances in predicting the number of infected and removed cases. In addition, we present an hybrid approach, AutoODE, that obtains a 57.4% reduction in mean absolute errors of the 7-day ahead COVID-19 trajectories prediction compared with the best deep learning competitor.
Robert Johansson, Numerical Python: Scientific Computing and Data Science Applications with Numpy, SciPy and Matplotlib, Apress, 2019. (code)
Leverage the numerical and mathematical modules in Python and its standard library as well as popular open source numerical Python packages like NumPy, SciPy, FiPy, matplotlib and more. This fully revised edition, updated with the latest details of each package and changes to Jupyter projects, demonstrates how to numerically compute solutions and mathematically model applications in big data, cloud computing, financial engineering, business management and more.
Justin Solomon, Numerical Algorithms: Methods for Computer Vision, Machine Learning, and Graphics, 1st Edition, A K Peters/CRC Press, 2015. (pdf)
Numerical Algorithms: Methods for Computer Vision, Machine Learning, and Graphics presents a new approach to numerical analysis for modern computer scientists. Using examples from a broad base of computational tasks, including data processing, computational photography, and animation, the textbook introduces numerical modeling and algorithmic design from a practical standpoint and provides insight into the theoretical tools needed to support these skills.
The book covers a wide range of topics—from numerical linear algebra to optimization and differential equations—focusing on real-world motivation and unifying themes. It incorporates cases from computer science research and practice, accompanied by highlights from in-depth literature on each subtopic. Comprehensive end-of-chapter exercises encourage critical thinking and build students’ intuition while introducing extensions of the basic material.
The text is designed for advanced undergraduate and beginning graduate students in computer science and related fields with experience in calculus and linear algebra. For students with a background in discrete mathematics, the book includes some reminders of relevant continuous mathematical background.
"Probabilistic Machine Learning" - a book series by Kevin Murphy
Kevin Patrick Murphy, Probabilistic Machine Learning: An Introduction, MIT Press, 2021. (Python codes)
新的版本，作者花了很多時間整理。以第一部分的數學基礎為例，使用機器學習說明相關的概念。也納入時事，例如 2.3.1 節的 Testing for COVID-19。
馬來西亞的學校提案，準備今年 5 月，開授相關線上演講，以便吸引高中生就讀資訊相關科系。去年12月中開校內協調會的時候，學校長官指派我，負責「數學與資訊工程 」。 月底到了，忙著提科技部計畫和撰寫研究的程式，但是，各種點子不斷地進入我的腦袋裡，只好趕緊把它寫下來，不然半夜進入我的夢鄉，擾人清夢。花了兩天，寫下初稿；最近又多次修正，前後花了不下 20 小時，決定提前定稿 ，以便改作其他教學和研究事務。
後來想一想， 既然這是一個有意義的工作，就準備把他錄成影片，並上傳 YouTube，以幫助有需要的年輕人。追求新知並傳授給學生，一直是我當老師快樂的泉源。
歡迎指正和提供寶貴意見。(pdf in 1) (pdf in 4，如果需要列印， 請雙面列印此版本，環保救地球)
初稿 2021/1/15。
Marc Peter Deisenroth, A. Aldo Faisal, and Cheng Soon Ong, Mathematics for Machine Learning, Cambridge University Press, 2020. (pdf)
The book assumes the reader to have mathematical knowledge commonly covered in high school mathematics and physics. For example, the reader should have seen derivatives and integrals before, and geometric vectors in two or three dimensions. Starting from there, we generalize these concepts. Therefore, the target audience of the book includes undergraduate university students, evening learners and learners participating in online machine learning courses.
Charu C. Aggarwal, Linear Algebra and Optimization for Machine Learning: A Textbook, Springer, 1st ed, 2020.
This textbook introduces linear algebra and optimization in the context of machine learning. Examples and exercises are provided throughout the book. A solution manual for the exercises at the end of each chapter is available to teaching instructors. This textbook targets graduate level students and professors in computer science, mathematics and data science. Advanced undergraduate students can also use this textbook. The chapters for this textbook are organized as follows:
為了減輕地震帶來的災害，各界都努力試圖以科技預測大地震的到來。而現在一款名為「MyShake」的 App，能夠利用智慧型手機的加速儀 (accelerometers)，偵測地震獨有的震動模式，提前警告用戶地震即將來襲，替用戶爭取到珍貴的避難時間！
Andy Kiersz 有進一步的說明。
