I like Casella and Berger, especially because it does so well at showing the connection between probability and statistics (first five chapters or so), but it should be noted that its approach is very different from the one in the slide deck above. Casella/Berger is purely frequentist rather than computational or Bayesian, and it spends a lot of time on likelihood theory and treats e.g. bootstrapping only very summarily.