Introduction to Algorithms (MIT)

This course provides an introduction to mathematical modeling of computational problems. It covers the common algorithms, algorithmic paradigms, and data structures used to solve these problems. The course emphasizes the relationship between algorithms and programming, and introduces basic performance measures and analysis techniques for these problems.

1: Algorithmic Thinking, Peak Finding

2: Models of Computation, Document Distance

3: Insertion Sort, Merge Sort

4: Heaps and Heap Sort

5: Binary Search Trees, BST Sort

6: AVL Trees, AVL Sort

7: Counting Sort, Radix Sort, Lower Bounds for Sorting

8: Hashing with Chaining

9: Table Doubling, Karp-Rabin

10: Open Addressing, Cryptographic Hashing

11: Integer Arithmetic, Karatsuba Multiplication

12: Square Roots, Newton’s Method

13: Breadth-First Search (BFS)

14: Depth-First Search (DFS), Topological Sort

15: Single-Source Shortest Paths Problem

16: Dijkstra

17: Bellman-Ford

18: Speeding up Dijkstra

19: Dynamic Programming I: Fibonacci, Shortest Paths

20: Dynamic Programming II: Text Justification, Blackjack

21: Dynamic Programming III: Parenthesization, Edit Distance, Knapsack

22: Dynamic Programming IV: Guitar Fingering, Tetris, Super Mario Bros.

23: Computational Complexity

24: Topics in Algorithms Research

Prerequisites: A firm grasp of Python and a solid background in discrete mathematics are necessary prerequisites to this course.

Summary of Main Course Features

Instructors: Prof. Erik Demaine and Prof. Srinivas Devadas

Required Textbook:

Suggested Textbook:

Downloads:

• Quiz 1 (PDF)
• Quiz 2 (PDF)
• Final exam (PDF)

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