Learning probability and statistics together I posted a question earlier where I mentioned that I am interested in learning Machine Learning but that my background in statistics and probability is pretty weak. 
Recently I previewed pages of 2 books which seem to quite suit my requirements. I was just wanting to know what the community thinks about my possible choices, given my background and goals. 
(1) All of Statistics: A Concise Course in Statistical Inference by Larry Wasserman. Looks nice to me but the author does not provide the answers to exercise problems (let alone a solution manual).
(2) A seemingly less well known but again a concise book: Probability and Statistics for Computer Scientists by Michael Baron
Any thoughts on these books or any good alternatives? Just to emphasize, I am a CS student looking to get to speed with probability and statistics for Machine Learning. I am at this point only looking for books suggestions not websites or videos. 
 A: I have the 8th edition of Modern Elementary Statistics which I see has a companion answers book to odd numbered questions. I also have an older version of Ott's Introduction to Statistical Methods and Data Analysis which I find incredibly useful to pointing out to colleagues about how I would love to see GLM models specified in journal articles. When I was studying, I found Harraway's book incredibly useful but sadly it never went to another edition. He's a really good writer that had good social science examples.
A: The first book "All of Statistics" is nice book to read. I dont know about second book. However, I would recommend the following book. It is really nice book to get an idea whats going on in statistics and probability.
A Modern Introduction to Probability and Statistics: Understanding Why and How (Springer Texts in Statistics)
by:  F.M. Dekking, C. Kraaikamp, H.P. Lopuhaä, L.E. Meester
A: I still like DeGroot / Schervish. Please, take a look:
http://www.amazon.com/Probability-Statistics-Edition-Morris-DeGroot/dp/0321500466
It is highly self-contained, starting with basic set theory, covering all the standard material on probability (without measure theory). After that, the inference part shines, with excellent explanations of sufficiency, estimation, the Neyman-Pearson lemma, hypothesis testing. He goes through nonparametric methods and ANOVA at the end of the book. DeGroot writing is not fast paced and is extremely clear. Since he was a Bayesian statistician (as is Schervish), the discussion of Bayesian topics is more detailed than we usually see in other books at the same level. The third edition covers simulation at the end, including MCMC tools.
A: I worked through the entire book about a year ago (2019) and put most of my solutions to Chapters 8–16 on github.
Please comment and correct me if I’m wrong, but I believe the free pdf released is a preprint, and the printed book is the official version. I used the free pdf (and included a copy of it in my git repo) but there will be some differences (I think some chapters are in a different order, some problems are different, etc), so keep that in mind if you decide to follow the physical text.
In my git repo I include a copy of the free pdf, and have some links pointing to old class versions that Larry taught at CMU. The homeworks, and included solutions, follow the text pretty closely, so they are helpful to look at when working through the text.
