Xiaodong Wang

This required lab is super straightforward, you just run some functions on MATLAB, and the instructions are pretty thorough. Not sure whether I really learned much from this lab, but it was cool to see what we were studying in the labs. The TAs are all super chill. You don't need to show up to the designated class times, but just complete all the labs on time.

Class has a lot of superficial straightforward content and professor makes his best to distinguish the most important parts but pretty dry about the questions. The class isn't these terrifying - there is truly a lot of content but it's nothing to think about - just literally do the same stuff over and over again.

Wang Lao Shi is amazing, she is such a wonderful teacher and a wonderful person. The class is a blast and really fun to be in. Chinese is a lot of work, but Wang Lao Shi makes it super fun. She is an amazing person and a great professor.

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 focus…

Let's get the bad news out of the way: 1. He's HEARTLESS. Do not expect any concilation from him. If you made an honest mistake and wrote on both sides of your formula sheet instead of only one, he will give you a 0, and will announce it to the class. If you plead him to count the final more, he'll just say "No, I'm not changing the grade breakdown." If you ask him if it's at all possible to pass the class, he'll reply with "You know the grade breakdown, go calculate your grade." He will also tell you that he does not curve, that he will not answer any question regarding what grade you deserved. 2. He thinks we're RETARDED. If you ask him some question that he considers dumb in class, he'll stare at you for a while, then elaborate by showing 2 more steps, and then say "If you don't understand how I went from here to here, drop the course." 3. The TA is a Chinese PhD student. That means unless you can speak Chinese to the TA, he's not very nice, and he'll hardly understand what you mean. Now the good news: 1. Tough guy has a soft side. On the last day of class, he will tell you that your final will override the midterm grade ON THE LAST DAY OF CLASS! So if you bomb the midterm, don't be alarmed -- you will get a second chance. He DOES curve. His curve is weird though. He calculates the grade that everyone is supposed to get, and then he shifts everyone's grade up. My real grade…

I really enjoyed Signals and Systems with Professor Wang. He is extremely clear and meticulous in his method of instruction, and if you truly show up to class and pay attention, his lectures are quite helpful. The homeworks, though quite tedious, prepare you extremely well for the exams. There is definitely a learning curve, but it is extremely rewarding in the end. Personally, this is the class that convinced me to pursue signal processing as a concentration. Overall, despite being a difficult class, I found it extremely worthwhile and Professor Wang's comprehensive instruction is a strong contributor to the course's value.

Wang is a terrible lecturer, doesnt know the material very well and simply reads off of pretty well made powerpoint slides. The amount you will learn from going to lecture is considerably disproportional to the 3 hours that you have to sit there listening to him. If you are planning to go, bring something else to do, unless you are planning on falling asleep. The schedule for the class was also very annoying, he ended up changing the dates of the first midterm, setting a new date for the second as a result and then changing that one as well. The material in this class is quite easy to grasp and can easily be done the night before a midterm or final. Homeworks are quite long and challenging at times. The course covers alot but there is only so much you can be tested on because alot of it requires a computer to do. It also becomes a lot easier if you come into it with a biology background. Most redeeming factor about Wang is that he loves to give out good grades and will try to maximize your final grade as best he can.

Biweekly problem sets, somewhat challenging. A lot are taken from papers in the IEEE information theory journal from the 1980s. Midterm covering first half of the material, final covering second half, both proof-based.

1 problem set a week that consists mostly of textbook questions and a few (around 2-3) matlab questions. The textbook questions can be tricky, especially if you understand what's going on in class, but the matlab questions are usually a joke.

Lawful good. In my opinion, this is how an engineering class should be structured. Xiaodong Wang followed the textbook, but curated important information and delivered them in a comprehensive and organized manner. His tests were fair, and if you did your homework, you would do well on them. Plenty of opportunity for practice was given, and the labs provided good engineering application to the theory learned in class. I learned a lot in his class and I think he’s a great professor.