My ML reading list
My ML reading list
My view of methods in ML is formed primarly by The Elements of Statistical Learning. A nice introduction (with a recent new edition) is An Introduction to Statistical Learning.
The books that I am trying to find time to go through are:
Machine Learning: A Probabilistic Perspective (and newer versions);
Artificial Intelligence: A Modern Approach on which is based an intro course in FMI.
Bulgarian researchers in probability (with similar sites) that I know of are E. Dimitrov, I. Hartarsky and L. Lichev.
Additionally, you can explore S. Apostolov's blog, which covers a wide range of intriguing mathematical subjects.
My master's thesis was going through Cutoff on all Ramanujan Graphs by E. Lubetzky and Y. Peres for which I clarified the proofs and found a non-trivial mistake (in Proposition 6, p<2). I was extremely lucky to be supervised by J. Salez.
I have written notes for the entry exam for the PhD program in FMI and for the exam after the first year and second year in it.
I often recommend the online course Financial Markets and the specialization Deep Learning. It turns out that Machine Learning in Coursera is the first course in the subject for a lot of people.