Reference List for Machine Learning and its Applications in Geotechnical Engineering - PART I: NEED TO KNOW KNOWLEDGE

1 Probability Theory
[1] Jeffreys, H. (1983). Theory of Probability. Oxford University Press, Oxford, New York.
[2] Sivia, D.S., and Skilling, J. (2006). Data Analysis: A Bayesian Tutorial. Oxford University Press, New York.
[3] Ang, A.H.-S., and Tang, W.H. (2007). Probability concepts in engineering, Vol. I Emphasis on applications to civil and environmental engineering, 2nd Ed. Wiley, New York.
[4] Beck, J.L. (2010). Bayesian system identification based on probability logic. Structural Control and Health Monitoring, 17(7), 825847.
[5] Yuen, K.V. (2010). Bayesian Methods for Structural Dynamics and Civil Engineering. John Wiley & Sons, ISBN: 978-0-470-82454-2.
[6] Gelman, A., et al. (2013). Bayesian Data Analysis, Third Edition. Chapman and Hall/CRC.
2 Information Theory
[7] Cover, T. M. and J. A. Thomas (1991). Elements of Information Theory. Wiley.
[8] MacKay, D. J. C. (2003). Information Theory, Inference and Learning Algorithms. Cambridge University Press.
3 Decision Theory
[9] Berger, J. O. (1985). Statistical Decision Theory and Bayesian Analysis (Second ed.). Springer.
[10] Bather, J. (2000). Decision Theory: An Introduction to Dynamic Programming and Sequential Decisions. Wiley.
4 Introductory Materials on Machine Learning
[11] Mitchell, T. (1997). Machine Learning. McGraw Hill.
[12] Bishop, C. M. (2006). Pattern Recognition and Machine Learning. Springer-Verlag New York.
[13] Barber, D. (2012). Bayesian Reasoning and Machine Learning. Cambridge University Press.
[14] Farrar, C. R., and Worden, K. (2012). Structural health monitoring: a machine learning perspective. John Wiley and Sons.
[15] Alpaydin, E. (2014). Introduction to Machine Learning, Third Edition. The MIT Press, London.