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

1. Algorithms and Computation.
2. Data Structures and Dynamic Arrays.
Introduction to Algorithms - Problem Session 1: Asymptotic Behavior of Functions and Double-ended....
3. Sets and Sorting.
4. Hashing.
Problem Session 2 (MIT 6.006 Introduction to Algorithms, Spring 2020).
5. Linear Sorting.
Problem Session 3.
6. Binary Trees, Part 1.
7. Binary Trees, Part 2: AVL.
Problem Session 4.
8. Binary Heaps.
9. Breadth-First Search.
Quiz 1 review.
10. Depth-First Search.
11. Weighted Shortest Paths.
Problem Session 5.
12. Bellman-Ford.
Problem Session 6.
13. Dijkstra.
Problem Session 7.
14. APSP and Johnson.
Quiz 2 Review.
15. Dynamic Programming, Part 1: SRTBOT, Fib, DAGs, Bowling.
16. Dynamic Programming, Part 2: LCS, LIS, Coins.
Problem Session 8.
17. Dynamic Programming, Part 3: APSP, Parens, Piano.
18. Dynamic Programming, Part 4: Rods, Subset Sum, Pseudopolynomial.
19. Complexity.
Quiz 3 Review.
20. Course Review.21. Algorithms—Next Steps.