This module covers methods for analyzing expected running time and average-case performance, randomized quicksort and quickselect, examples of randomized algorithms and data structures, and "high probability" bounds. [Workload: 1.5 Weeks]

This module covers techniques for analyzing recursive algorithms ("divide and conquer"), recursive thinking, and examples of recursion in algorithm design. [Workload: 1.5 Weeks]

This module covers priority queues, binary search trees for representing sets, maps, and sequences, randomized and amortized tree balancing mechanisms, and sweep line methods. [Workload: 1.5 Weeks]

This module covers multi-dimensional search structures, amortization, memory-conscious structures (B-trees), and skip lists. [Workload: 2 Weeks]

This module covers hash tables and distributed hash tables, universal hashing, polynomial hashing of large objects, and applications of hashing across different computing subfields. [Workload: 1.5 Weeks]

This module covers greedy algorithms, dynamic programming, heuristics based on iterative refinement, multi-scale methods, pruned exhaustive search, and hard problems and approximation algorithms. [Workload: 2 Weeks]

This module covers gradient descent and Newton's method, stochastic gradient descent, gradient-free methods, continuous relaxations of discrete problems, convexity, optimization with constraints, and common types of optimization problems. [Workload: 1.5 Weeks]

This module covers connectivity and related problems, shortest paths, and matchings. [Workload: 1.5 Weeks]

This module contains the Final Exam for the Design and Analysis of Algorithms course. [Workload: 1 Week]

In this module, you will find recordings from Office Hours.