Most of my blog posts have prerequisites stated at the beginning of the post. If you are unfamiliar with one or another, please refer to the following table for recommended readings.

I only list references that I found helpful, so it is by no means exhaustive nor objective, and you might find better references. If you do, please let me know! The table will be updated incrementally.

TopicLight introductionDeep dive
AI alignment problemSee "AI Safety"See "AI Safety"
AI safetyBostrom
Eliezer Yudkowski's presentation
Leike et al. 
Everitt et al. 
alignmentforum.org *
Bayesian statisticsMacKay 
game theoryLeyton-Brown and Shoham *
Gaussian processesDavid MacKay's lecture (slides and alternative upload here)Rasmussen and Williams  *
neural networks3blue1brown’s series of videos
reinforcement learningDavid Silver's lecture series
Berkley deep reinforcement learning lecture series*
Sutton and Barto 
LSTMsChristopher Olah's blog post
Neural processesGarnelo et al. 
information theoryMacKay 
classical mechanicsLandau and Lifshitz 
relativityEinstein's wonderful nearly-equation-free book Wald 
Misner et al. 
Penrose and Rindler 

Sources with a “*” are those which I have only read partially.

References

1.
Wald, R. M. General relativity. (University of Chicago Press, 1984).
1.
Misner, C. W., Thorne, K. S. & Wheeler, J. A. Gravitation. (W. H. Freeman, 1973).
1.
Einstein, A. Relativity : the special and general theory. (2005).
1.
Penrose, R. & Rindler, W. Spinors and space-time. 1, (Cambridge University Press, 1984).
1.
Landau, L. D. & Lifshitz, E. M. Mechanics. (Elsevier, 1982).
1.
Leyton-Brown, K. & Shoham, Y. Essentials of Game Theory: A Concise Multidisciplinary Introduction. Synthesis Lectures on Artificial Intelligence and Machine Learning 2, 1–88 (2008).
1.
Rasmussen, C. E. & Williams, C. K. I. Gaussian processes for machine learning. (MIT Press, 2008).
1.
Everitt, T., Lea, G. & Hutter, M. AGI Safety Literature Review. arXiv:1805.01109 [cs] (2018).
1.
Bostrom, N. Superintelligence: paths, dangers, strategies. (Oxford University Press, 2014).
1.
Leike, J. et al. Scalable agent alignment via reward modeling: a research direction. arXiv:1811.07871 [cs, stat] (2018).
1.
Garnelo, M. et al. Neural Processes. arXiv:1807.01622 [cs, stat] (2018).
1.
Sutton, R. S. & Barto, A. G. Reinforcement Learning: An Introduction.
1.
MacKay, D. J. C. Information Theory, Inference, and Learning Algorithms. (Cambridge University Press, 2003).