| Lecture 1 |
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Topics: (no slides)
- Formal introduction
- Course details
- Syllabus
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| Lecture 2 |
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- Introductio to Asymptotics Big-O/Ω/Θ, growth rates, amortized idea; examples.
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| Lecture 3 |
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- Intro + asymptotics Big-O/Ω/Θ, growth rates, amortized idea; examples.
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| Lecture 4 |
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Topics: (slides)
- Recurrences substitution, recursion tree, Master theorem; practice set.
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| Lecture 5 |
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Topics: (slides)
- Recurrences substitution, recursion tree, Master theorem; practice set.
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| Lecture 6 |
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Topics: (slides)
- Proof techniques induction, invariants, exchange argument; correctness templates
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| Lecture 7 |
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Topics: (slides)
- Proof techniques induction, invariants, exchange argument; correctness templates
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| Lecture 8 |
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Topics: (slides)
- Divide & conquer I merge sort, quicksort; partition; expected complexity
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| Lecture 9 |
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Topics: (slides)
- Divide & conquer I merge sort, quicksort; partition; expected complexity
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| Lecture 10 |
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Topics: (slides)
- Divide & conquer II binary search variants, selection (quickselect), closest pair (concept).
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| Lecture 11 |
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Topics: (slides)
- Divide & conquer II binary search variants, selection (quickselect), closest pair (concept).
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| Lecture 12 |
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Topics: (slides)
- Heaps & priority queues heap operations; heapsort; applications
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| Lecture 13 |
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Topics: (slides)
- Heaps & priority queues heap operations; heapsort; applications
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| Lecture 14 |
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Topics: (slides)
- Hashing hash tables, collision handling, universal hashing intuition
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| Lecture 15 |
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Topics: (slides)
- Hashing hash tables, collision handling, universal hashing intuition
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| Lecture 16 |
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Topics: (slides)
- Balanced search trees (overview) AVL/Red-black intuition; order statistics trees idea
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| Lecture 17 |
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Topics: (slides)
- Balanced search trees (overview) AVL/Red-black intuition; order statistics trees idea
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| Lecture 18 |
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Topics: (slides)
- Greedy I activity selection, interval scheduling; proving greedy (exchange).
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| Lecture 19 |
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Topics: (slides)
- Greedy I activity selection, interval scheduling; proving greedy (exchange).
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| Lecture 20 |
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Topics: (slides)
- Greedy II Huffman coding, minimum spanning tree (Kruskal/Prim) correctness
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(Feb 25) Last Date for Proposal Submission. |
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Mid Semester Exam Week |
Best of Luck.
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| Lecture 21 |
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Topics: (slides)
- Greedy II Huffman coding, minimum spanning tree (Kruskal/Prim) correctness
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| Lecture 22 |
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Topics: (slides)
- Dynamic programming I DP templates; memoization vs tabulation; knapsack
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| Lecture 23 |
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Topics: (slides)
- Dynamic programming I DP templates; memoization vs tabulation; knapsack
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| Lecture 24 |
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Topics: (slides)
- Dynamic programming II LIS, edit distance; state design and optimization tricks
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| Lecture 25 |
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Topics: (slides)
- Dynamic programming II LIS, edit distance; state design and optimization tricks
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| Lecture 26 |
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Topics: (slides)
- Graph algorithms I BFS/DFS, topological sort; DAG shortest paths
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| Lecture 27 |
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Topics: (slides)
- Graph algorithms I BFS/DFS, topological sort; DAG shortest paths
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| Lecture 28 |
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Topics: (slides)
- Graph algorithms II shortest paths (Dijkstra, Bellman–Ford); negative cycles
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| Lecture 29 |
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Topics: (slides)
- Graph algorithms II shortest paths (Dijkstra, Bellman–Ford); negative cycles
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| Lecture 30 |
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Topics: (slides)
- Graph algorithms III all-pairs (Floyd–Warshall); applications.
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| Lecture 31 |
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Topics: (slides)
- Graph algorithms III all-pairs (Floyd–Warshall); applications.
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| Lecture 32 |
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Topics: (slides)
- Network flow max flow/min cut; Ford–Fulkerson/Edmonds–Karp; bipartite matching
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(Mar 30) - Last Date of Mid Presentation. |
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| Lecture 33 |
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Topics: (slides)
- Network flow max flow/min cut; Ford–Fulkerson/Edmonds–Karp; bipartite matching
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| Lecture 34 |
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Topics: (slides)
- NP-completeness P vs NP, reductions, NP-complete examples; what it means practically
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| Lecture 35 |
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Topics: (slides)
- NP-completeness P vs NP, reductions, NP-complete examples; what it means practically
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| Lecture 36 |
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Topics: (slides)
- Approximation & heuristics vertex cover/knapsack ideas; PTAS intuition; local search
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| Lecture 37 |
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Topics: (slides)
- Approximation & heuristics vertex cover/knapsack ideas; PTAS intuition; local search
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| Lecture 38 |
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Topics: (slides)
- Randomized algorithms randomized quicksort, hashing; Monte Carlo vs Las Vegas
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| Lecture 39 |
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Topics: (slides)
- Randomized algorithms randomized quicksort, hashing; Monte Carlo vs Las Vegas
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| Lecture 40 |
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Topics: (slides)
- Mixed paradigm problems and Solution
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| Lecture 41 |
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| Lecture 42 |
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Topics: (slides)
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(Apl 30) Last Date of Codes Submission. |
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(Apl 30) Dead Line for Final Presentation Video Submission. |
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(Apl 30) Dead Line for Report Submission. |
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End Semester Exam Week |
Best of Luck.
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Link Added on Last Date for Submission :
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DA111
