Semester.ly

Johns Hopkins University | EN.600.464

Randomized and Big Data Algorithms

3.0

credits

Average Course Rating

(4.7)

The course emphasizes algorithmic design aspects, and how randomization can be a helpful tool. The topics covered include: tail inequalities, linear programming relaxation & randomized rounding, de-randomization, existence proofs, universal hashing, markov chains, metropolis and metropolis-hastings methods, mixing by coupling and by eigenvalues, counting problems, semi-definite programming and rounding, lower bound arguments, and applications of expanders. [Analysis] (www.cs.jhu.edu/~cs464) Recommended Course Background: EN.600.363 or EN.600.463.

Fall 2012

(4.83)

Fall 2014

(4.56)

Fall 2012

Professor: S Kosaraju

(4.83)

Students said the professor was a great teacher who was passionate about the subject and very wil ing to help students with any questions they had. They did not like the very heavy workload. Students suggested reducing some of the assignments or making the class worth more credits to compensate for the high workload. Overall, students recommended this as a very rewarding class, but say prospective students should be prepared to spend a lot of time on the homework and studying for exams.

Fall 2014

Professor: Vladimir Braverman

(4.56)

Students thought the best aspect of this course was the effective teaching of the engaging instructor who taught a comprehensive introduction to the subject matter. Students expressed that their least favorite aspect of the course was the heavy workload of homework assignments. Some students thought the course could be improved by reducing the workload or by reducing the topics covered and even expanding the class to two semesters. They thought it was most important for others considering taking the class to know that the course required a substantial time commitment and that they should only take the class alongside classes with light workloads.