I'm also surprised Jeff Erickson's free lecture notes [3] aren't there given 1) its easy to remember domain 2) its incredibly high, practical quality. Practical because I've had interviews that just grab questions from the book, and also because his course is basically just a walk-through of the book. It's also very easy to read, and although I didn't do great in his class, his conceptual lessons still stick with me.
Couldn't recommend Jeff Erickson's lecture notes more for algorithms, DP, and the like. I too did rather poorly in the class so you're not alone! In a similar vein, Lawrence Angrave, a systems programming lecturer, has a wonderful crowd-sourced "book" [1] on all things systems programming. It is my go to resource for brushing up on these topics. Lastly, David Forsyth, a statistics/applied ML lecturer has a gold mine of a book for diving into ML and difficult concepts that come with it [2].
[1] https://github.com/angrave/SystemProgramming/wiki
[2] http://luthuli.cs.uiuc.edu/~daf/courses/AML-18/learning-book...
I'm also surprised Jeff Erickson's free lecture notes [3] aren't there given 1) its easy to remember domain 2) its incredibly high, practical quality. Practical because I've had interviews that just grab questions from the book, and also because his course is basically just a walk-through of the book. It's also very easy to read, and although I didn't do great in his class, his conceptual lessons still stick with me.
[1] http://acs.pub.ro/~cpop/SMPA/Computer%20Architecture%20A%20Q...
[2] https://github.com/Seanforfun/Books/blob/master/Computer/Com...
[3] http://algorithms.wtf