AI書單推薦

這篇介紹我看過的AI書籍中，覺得很棒的書單，我按照不同的AI作法來分類：

Machine Learning:

1. Pattern Recognition and Machine Learning, Christopher M. Bishop, 2011
2. The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition,  Trevor Hastie , Robert Tibshirani, Jerome Friedman, 2009
3. An Introduction to Statistical Learning: with Applications in R, Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani, 2021
4. Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems,  Aurélien Géron, 2022
5. 統計學習方法, Second Edition, 李航, 2019
6. 機器學習, 周志華, 2016
7. 機器學習方法, 李航, 2022

Deep Learning:

1. Deep Learning,  Ian Goodfellow, Yoshua Bengio, Aaron Courville, 2016
2. Deep Learning with Python, Second Edition,  Francois Chollet, 2021

Unsupervised Learning:

1. Hands-On Unsupervised Learning Using Python, Ankur Patel, 2019

Reinforcement Learning:

1. Dynamic Programming and Optimal Control (2 Vol Set), Dimitri P. Bertsekas, 2012
2. Reinforcement Learning and Optimal Control, Dimitri P. Bertsekas, 2019
3. Rollout, Policy Iteration, and Distributed Reinforcement Learning, Dimitri P. Bertsekas, 2020
4. Neuro-Dynamic Programming, Dimitri P. Bertsekas, John N. Tsitsiklis, 1996
5. Deep Reinforcement Learning Hands-On - Second Edition, Maxim Lapan, 2020
6. Reinforcement Learning, second edition,  Richard S. Sutton, Andrew G. Barto, 2018
7. Statistical Reinforcement Learning: Modern Machine Learning Approaches, Masashi Sugiyama, 2015

要學會AI，尚需要一些周邊技能，這邊我推薦：

1. Elements of Information Theory 2nd Edition, Thomas M. Cover, Joy A. Thomas, 2006
2. Nonlinear Programming 3rd Edition, Dimitri P. Bertsekas, 2016
3. Numerical Optimization 2nd Edition, Jorge Nocedal, Stephen Wright, 2006
4. Fundamentals of Statistical Signal Processing, Volume I: Estimation Theory, Steven Kay, 1993
5. Adaptive Signal Processing, Bernard Widrow, 1985

這些都是我自己看完 (花了七年)，覺得很棒的書單，讓大家少走一些冤枉路。

不同的程度有不同的閱讀順序，若大家有閱讀順序上的疑慮，我可以再進一步補充。