- Machine Learning and Pattern Recognition, by Christopher Bishop
- Elements of Statistical learning, by Trevor Hastie, et al.
- Bayesian Data Analysis, by A. Gelman, et al.
- Feedback Systems, by K.J. Aström and R.M. Murray
- Elements of Information Theory, by T.M. Cover and J.A. Thomas
- Statistics Explained：An introductory Guide for Life Scientists, by Steve McKillup
- Probability: The Logic of Science, by E. T. Jaynes
- Deep Learning, by I. Goodfellow, Y. Bengio and A. Courville
Note that you can find most of the excellent books free online.
- Convolutional Neural Networks for Visual Recognition, Feifei Li, Stanford University, http://cs231n.github.io/
- Neural Networks for Machine Learning, G. Hinton, Toronto University, https://www.coursera.org/learn/neural-networks
- Machine Learning, Andrew Ng, Stanford University, https://www.coursera.org/learn/machine-learning
- Python Numpy Tutorials, Stanford University, http://cs231n.github.io/python-numpy-tutorial/