Introduction to Algorithms and Complexity

Description: Lecture, four hours; discussion, two hours; outside study, six hours. Enforced requisites: course 32, Mathematics 61. Designed for junior/senior Computer Science majors. Introduction to design and analysis of algorithms. Design techniques: divide-and-conquer, greedy method, dynamic programming; selection of prototypical algorithms; choice of data structures and representations; complexity measures: time, space, upper, lower bounds, asymptotic complexity; NP-completeness. Letter grading.

Units: 4.0
1 of 1
Overall Rating 3.7
Easiness 2.5/ 5
Clarity 3.7/ 5
Workload 2.6/ 5
Helpfulness 4.0/ 5
Most Helpful Review
Meka is organized and nice, but he assumed we knew a lot more coming into the class than we actually did. He would present topics without a lot of lead-up, so you'd be suddenly looking at things like advanced probability without having taken any statistics classes (and even the people who had taken those classes said that they'd never seen before the material Meka was presenting). Classes are all about new material, but there wasn't a very cohesive chain of applicability for all the different topics, so it made it hard to absorb the info; it just seemed like a big bag of difficult, seemingly disjointed material. Meka's a nice guy, but he tended to not tell you how to do things for fear of "giving away the answer". Consequently, any methods you developed to solve any questions was of your own doing. If Sean is still TAing, he's a big help. Overall, I feel like the class was unnecessarily hard and you didn't leave feeling like you had new tools in your coding arsenal; you just left feeling glad that it was all over. If you are in this class, here are some things that can help: He sticks fairly close to the book, so if you can read the chapters before lecture, you’ll be ready to hear his advanced versions of the material. The homeworks were ridiculously hard, but once you have the answers (TA help…), really understand how you got there, because his exam questions are often just versions of those HW questions (and/or versions of some proof he did in class). He really expected us to reference algorithms/proofs he did in lecture. If you can remember all those, you only need to add “blah blah algorithm/proof, as shown in lecture” much of the time for full points. In fact, NOT referencing one of those can often wipe points off your HW/exam even though you did everything else right. Overall, the HW grading was up and down (high average for the class on one assignment, then an inexplicably, drastically low average on the next) and we often weren’t sure what constituted a “correct answer” because the instructions were vague, yet the grading was very specific, like a N Campus class looking for you to mention key words to match the grading rubric. Like I said, perhaps his teaching methods will change and he did grade fairly with the final grades, but I would recommend someone else if you want to really “get” algorithms.
Overall Rating N/A
Easiness N/A/ 5
Clarity N/A/ 5
Workload N/A/ 5
Helpfulness N/A/ 5
1 of 1

Adblock Detected

Bruinwalk is an entirely Daily Bruin-run service brought to you for free. We hate annoying ads just as much as you do, but they help keep our lights on. We promise to keep our ads as relevant for you as possible, so please consider disabling your ad-blocking software while using this site.

Thank you for supporting us!