MIT OpenCourseWare’s 6.006 Introduction to Algorithms is a comprehensive undergraduate course on designing, analyzing, and implementing efficient algorithms. It begins with mathematical modeling of computational problems and performance analysis, particularly asymptotic complexity, then develops fundamental data structures including dynamic arrays, hash tables, binary search trees, AVL trees, and heaps. The course also covers major algorithmic techniques for sorting, searching, and graph problems, including breadth-first search, depth-first search, shortest-path algorithms such as Bellman–Ford and Dijkstra, and all-pairs shortest paths.
A major theme is how to think about algorithmic problems, rather than simply memorizing algorithms. Students learn to prove correctness and analyze efficiency, reduce new problems to familiar ones, and design algorithms using brute force, divide-and-conquer, dynamic programming, and greedy/incremental approaches. The later lectures introduce complexity theory, including polynomial time, pseudopolynomial algorithms, NP, NP-hardness, and NP-completeness. The course is unusually complete as a self-study resource, providing lecture videos, detailed notes, problem sets, solutions, quizzes, and exams.
MIT 6.006 — Introduction to Algorithms
A major theme is how to think about algorithmic problems, rather than simply memorizing algorithms. Students learn to prove correctness and analyze efficiency, reduce new problems to familiar ones, and design algorithms using brute force, divide-and-conquer, dynamic programming, and greedy/incremental approaches. The later lectures introduce complexity theory, including polynomial time, pseudopolynomial algorithms, NP, NP-hardness, and NP-completeness. The course is unusually complete as a self-study resource, providing lecture videos, detailed notes, problem sets, solutions, quizzes, and exams.
MIT 6.006 — Introduction to Algorithms
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