Course Contents: Topics include
Algorithmic fundamentals: mathematical foundations, growth of functions, recurrences (6 hrs)
Sorting and order statistics (6 hrs)
Data structures: heap, binary search trees, RB trees, disjoint sets (4 hrs)
Advanced design and analysis techniques: dynamic programming, greedy algorithms, amortized analysis
(9 hrs)
Graph algorithms: graph representations, searching, minimum spanning trees, shortest paths, network
flow, matching (14 hrs)
Computational complexity, NP-completeness, and approximation algorithms (6 hrs)
General-purpose algorithms: simulated annealing and machine learning, as time permits.