Lectures

1. Lecture 01 - Unsorting: String operations. Introduction to Unsorting.
   Notes: pdf
2. Lecture 02 - Unsorting: Binary reflected Gray code. Non-existence of swap Gray codes for fixed-weight binary strings.
   Notes: pdf Wikipedia
3. Lecture 03 - Unsorting: Permutations in zig-zag order.
   Notes: pdf Wikipedia
4. Lecture 04 - Unsorting: Permutations in barber's pole order.
   Notes: pdf
5. Lecture 05 - Unsorting: Recursive programs.
   Notes: pdf
   Programs: binary.py Gray.py combo.py binaryPartition.py GrayPartition.py
6. Lecture 06 - Unsorting: Successor programs.
   Notes: pdf
   Programs: nextBinary.py nextPerm.py nextCool.py
7. Lecture 07 - Graphs: Dijkstra's algorithm.
   Notes: pdf Chapter 24.2-24.3
8. Lecture 08 - Graphs: Floyd-Warshall algorithm. Kruskal's algorithm.
   Notes: pdf Chapter 25.2 and 23
9. Lecture 09 - Graphs: Prim's algorithm.
   Notes: pdf Chapter 23
10. Lecture 10 - Network Flows: Max-flow = min-cut.
    Notes: pdf Chapter 26.1-26.2
11. Lecture 11 - Network Flows: Generalizations of model (parallel edges, multiple sources).
    Notes: pdf Chapter 26.1-26.2
12. Lecture 12 - Network Flows: Bipartite matching.
    Notes: pdf Chapter 26.3
13. Lecture 13 - Strings: Substring search including Knuth Morris Pratt.
    Chapter 32 (focus on 32.3 and 32.4)
14. Lecture 14 - Strings: Huffman encoding.
    Notes: pdf and Chapter 16.3
15. Lecture 15 - Data Structures: Priority Queues using Binary Heaps.
    Chapter 6
16. Lecture 16 - Data Structures: Disjoint sets.
    Chapter 21
17. Midterm/Assignment review
18. Midterm
19. Lecture 17 - Data Structures: Dictionaries. (Self-Balancing) binary search trees.
    Notes: pdf Chapter 12
20. Lecture 18 - Data Structures: 2-3 trees.
    Notes: pdf
21. Lecture 19 - Approximation Algorithms: Vertex cover.
    Notes: pdf and Chapter 35.1
22. Lecture 20 - Approximation Algorithms: TSP.
    Notes: pdf and Chapter 35.2
23. Lecture 21 - Algorithm Types: Greedy, Dynamic Programming, Divide and Conquer for Max Subarray
    Notes: pdf and Chapters 4.1, 15.3, 16.2
24. Lecture 22 - Analysis: Substitution Method
    Notes: pdf and Chapter 4.3
25. Lecture 23 - Analysis: Recursion Tree Method and Master Method
    Notes: pdf and Chapter 4.4-4.5
26. Lecture 24 - Analysis: Amortized Analysis
    Notes: pdf and Chapter 17.1-17.3