Topics in Computer Science: Cloud Computing and Big Data · Computer Science
The coding homeworks are not bad at all. A few ungraded exercises here and there to help with conceptual content absorption. The exams are not supposed to be bad, but they give you a tiny bit of anxiety because the exams are basically the first time when you apply the theoretical concepts learned in class. Professors like to do this... what can I say.
4 homework assignments 1 lecture to scribe 1 final project based on reading and original research
Sporadic homework (10%) Scribe notes for one class (30%) Final project (40%)
60% HW - including two large "data challenges" - heavy 15% "Midterm" the last day of class - not difficult 25% Final Project - difficult to do well
As much as you want it to be. Your grade is determined from participation (it is a seminar/discussion based class after all, so provide critiques and comments on others' ideas), and your final app + presentation/summary report. It would be smart to have your app mostly functional a couple weeks before the end of the semester so you aren't rushing to finish it on top of the millions of other projects you'll be dealing with at the end of the semester :)
Reading reflections each class 2 hard homework assignments 1 lecture to give 1 lecture to scribe 1 final project based on reading and original research
As hard as you want to be(Want to learn more? Make sure you spend a lot of time in this course)
10 papers 2 quizzes 3 small projects 1 final project
Though I have taken statistics and probability courses in the past, this course did a great job introducing me to new probability tools that are especially relevant to theoretical computer science. After defining basic information theory concepts, we applied them to the study of communication complexity, algorithm lower bounds, interactive compression, and data structures problems. The class convinced me that information theory is the "right" language to describe probability in computer science. In terms of instruction, Omri did a pretty good job. He provided intuition for each definition and proof. However, I feel like he spent a little too long on introductory material; given this was a 6000-level class, I think most of the students would have been fine skipping a good chunk of introductory material, and we could have covered more.
Great topics course from a great professor. Rocco is great at concrete complexity! Comprehensive survey of different low-level computational models (Boolean formulas, decision trees, branching programs, and constant depth circuits) and representative lower bounds in each model. We used communication complexity, linear algebra, and Fourier analysis of the hypercube as tools throughout. He proved all the results he presented, which was impressive: usually it is hard to present state-of-the art results due to the length of the proofs. Very convincing and thorough.