Some books and information on machine learning and AI

General introduction (without math)

• 簡禎富，工業3.5：台灣企業邁向智慧製造與數位決策的戰略，天下雜誌，2019
• Alex J. Gutman and Jordan Goldmeier, Becoming a Data Head: How to Think, Speak, and Understand Data Science, Statistics, and Machine Learning, Wiley, 2021.
• John Maccormick, Nine Algorithms That Changed the Future: The Ingenious Ideas That Drive Today’s Computers, Princeton Univ Press, 2013. (陳正芬譯，改變世界的九大演算法：讓今日電腦無所不能的最強概念，經濟新潮社，2014)
• Cade Metz, Genius Makers: The Mavericks Who Brought AI to Google, Facebook, and the World, Dutton, 2021. (王曉伯譯，AI製造商沒說的祕密: 企業巨頭的搶才大戰如何改寫我們的世界?，時報文化，2022)
• Andrew Ng, How to Build Your Career in AI, 2023. (Free ebook, new)
• Annalyn Ng and Kenneth Soo  , Numsense! Data Science for the Layman: No Math Added, 2017. (沈佩誼譯，文科生也看得懂的資料科學，碁峰，2018)
• Cathy O'Neil, Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy, 2016 (許瑞宋譯，大數據的傲慢與偏見：一個「圈內數學家」對演算法霸權的警告與揭發，大寫出版，2017)

Podcast: 

• 簡禎富，IC之音｜藍湖策略．數位轉型 (知識含量很高的節目，眼睛休息的時候，可以聽一下，以了解產業現況)
• Causal Bandits Podcast by Alex Molak
• The Robot Brains Podcast hosted by Pieter Abbeel

Programming tools

• Allen B. Downey, Elements of Data Science, No Starch Press, 2023.
• Aurélien Géron, Hands-on Machine Learning with Scikit-Learn, Keras and TensorFlow, O'Reilly, 3rd edition, 2022. (Python, nice mathematical introduction)
• Shlomo Kashani and Amir Ivry, Deep Learning Interviews: Hundreds of fully solved job interview questions from a wide range of key topics in AI, arXiv:2201.00650. (It is a nice review and checking after you take machine learning courses.)
• Wes McKinney, Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython, O'Reilly Media, 3rd edition, 2022. (Creator of Pandas)

Broad introduction 

• Dimitris Bertsimas, Allison K. O'Hair, and William R. Pulleyblank, The Analytics Edge, Dynamic Ideas LLC.
• Required course for the Master of Business Analytics at MIT
• Foster Provost and Tom Fawcett, Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking,  O'Reilly Media, 1st edition, 2013.
• Stuart J. Russel and Peter Norvig, Artificial Intelligence: A Modern Approach, Pearson, 4th edition, 2020.

Graduate ML books 

• 李家岩、洪佑鑫，製造數據科學：邁向智慧製造與數位決策，前程文化，2022 (「製造數據科學」的課程網頁)
• Bradley Efron and Trevor Hastie, Computer Age Statistical Inference: Algorithms, Evidence and Data Science, Cambridge University Press, 2016.
• Trevor Hastie, Robert Tibshirani, and Jerome Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer, Second Edition, February 2009.
• Trevor Hastie, Robert Tibshirani, and Martin Wainwright, Statistical Learning with Sparsity: the Lasso and Generalizations, Chapman & Hall, 2015.
• Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani, Jonathan Taylor, An Introduction to Statistical Learning: With Applications in R and Python. (new)
• Jure Leskovec, Anand Rajaraman, Jeff Ullman, Mining of Massive Datasets, Cambridge University Press, 3rd edition, 2020. (courses, book material)
• Kevin Patrick Murphy, Probabilistic Machine Learning: An Introduction, MIT Press, 2022. 
• Part I on foundational math tools, Table of content
• Endorsements
• Kevin Patrick Murphy, Probabilistic Machine Learning: Advanced Topics, MIT Press, 2023. 
• Talk at Princeton
• Table of content
• Endorsements: The chapter on generative models is a masterpiece. -- Geoff Hinton
• A. Ng, Machine Learning Specialization (more courses by Ng)
• Rudin, Cynthia. Intuition for the Algorithms of Machine Learning, Self-pub, eBook, 2020. (Videos) (new)
• The analysis of a ML problem usually depend on optimization. 

Deep learning

• David Foster, Generative Deep Learning, 2nd Edition, O’Reilly, May 2023 (code)
• With appropriate math and pictorial explanation, this is a nice book to learn about this important field. 
• Ian Goodfellow, Yoshua Bengio, and Aaron Courville, Deep Learning, MIT Press, 2016.
• Aston Zhang, Zachary C. Lipton, Mu Li, Alexander J. Smola, Dive into Deep Learning,  Cambridge University Press, 2023.  

Optimization and machine learning 

• Dimitris Bertsimas and Jack Dunn, Machine Learning Under a Modern Optimization Lens, Dynamic Ideas LLC, 2019.
• Required course for the Master of Business Analytics at MIT
• Dimitris Bertsimas and Dick Den Hertog, Robust and Adaptive Optimization, Dynamic Ideas, 2022.
• Omar Skali Lami, Predictive and Prescriptive Analytics in Operations Management, MIT doctoral dissertation, 2022.
• I.S. Paskov, Stable Machine Learning, MIT, doctoral dissertation, 2022. (new)

Reinforcement Learning

• Dimitri Bertsekas, Many (free) books
• Prof. Bertsekas is one of my favorie authors. 
• DeepMind and UCL, reinforcement learning
• Mykel J. Kochenderfer, Tim A. Wheeler, and Kyle H. Wray, Algorithms for Decision Making, MIT Press, 2022.
• Free book, Julia codes 
• Pascal Poupart, CS885 Reinforcement Learning (lecture notes and videos) (new)
• With background in machine learning and optimization, you could master the material in this course. 
• Richard S. Sutton and Andrew G. Barto, Reinforcement Learning: An Introduction, second edition, MIT Press, 2018.