论文信息 - AI education: machine learning resources

AI education: machine learning resources

In this column, we focus on resources for learning and teaching three broad categories of machine learning (ML): supervised, unsupervised, and reinforcement learning. In our next column, we will focus specifically on deep neural network learning resources, so if you have any resource recommendations, please email them to the address above.

Todd W. Neller

[1] Peter Vrancx,et al. Reinforcement Learning: State-of-the-Art , 2012 .

[2] David Barber,et al. Bayesian reasoning and machine learning , 2012 .

[3] Kevin P. Murphy,et al. Machine learning - a probabilistic perspective , 2012, Adaptive computation and machine learning series.

[4] Andrew G. Barto,et al. Reinforcement learning , 1998 .

[5] Ian H. Witten,et al. Data mining - practical machine learning tools and techniques, Second Edition , 2005, The Morgan Kaufmann series in data management systems.

[6] Ian Witten,et al. Data Mining , 2000 .

[7] Christopher M. Bishop,et al. Pattern Recognition and Machine Learning (Information Science and Statistics) , 2006 .

[8] Csaba Szepesvári,et al. Algorithms for Reinforcement Learning , 2010, Synthesis Lectures on Artificial Intelligence and Machine Learning.

[9] Richard S. Sutton,et al. Introduction to Reinforcement Learning , 1998 .

[10] Robert Tibshirani,et al. The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Edition , 2001, Springer Series in Statistics.

[11] Peter Norvig,et al. Artificial Intelligence: A Modern Approach , 1995 .

[12] Daniela M. Witten,et al. An Introduction to Statistical Learning: with Applications in R , 2013 .