Prof Wang isn't the most animated lecturer and the room gets hot and it's Friday and it's easy to start dozing off. None of that changes the fact that information theory is BEAUTIFUL. It's easy to get lost in the mathematics (of which there is a lot - more on that in a bit). I highly recommend taking some time after you get home and try to digest what you're learning, get an intuitive feel for what entropy and channel capacity and "information" are, and you will find your mind blown time and time again. The idea that something as "dumb" and taken for granted as zipping a file is (asymptotically) equivalent to something as profound as automatically finding all the structure in a dataset is at first unfathomable but becomes obvious as you go along and has very exciting implications. Many of the proofs and algorithms you learn about (Blahut-Arimoto, reverse water-filling, Lempel-Ziv) are extremely aesthetically appealing as well. Unfortunately some of the proofs are quite tedious.
Don't let the department fool you, this is not an engineering class (despite being taught by an IEEE Fellow) but a mathematics class. The registrar is lying when it says the prereq is just "probability"; the real prerequisites should be STAT4105+4107 (probability and statistics), ELEN4815 (random signals and noise; not obligatory but good to have), and real analysis.
Unfortunately the class is not focused as much on interpretation as I would have liked. Cover and Thomas is an excellent textbook and a pleasure to read. I also found David MacKay's videos from Cambridge helpful for intuition for the first few weeks but be warned, this class goes a lot deeper into information theory the Cambridge class.