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Algorithms: Design, Analysis, and Implementation

A rigorous, implementation-oriented course that follows the chapter ordering of Cormen et al.’s Introduction to Algorithms (4th ed.). Students learn to design, analyze, and code fundamental algorithms and data structures, progressing from sorting and search to graph algorithms, dynamic programming, and advanced topics like network flow and NP-completeness. Each lesson pairs theory with hands-on Python/Java exercises and asymptotic analysis, preparing students for technical interviews, competitive programming, and graduate-level coursework.

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Foundations & Asymptotic Analysis

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RAM Model, Pseudocode, and Big-O appears earlier in the syllabus and supports Merge-Sort: Algorithm & Analysis.Merge-Sort: Algorithm & Analysis appears earlier in the syllabus and supports Quick-Select & Median of Medians.Quick-Select & Median of Medians appears earlier in the syllabus and supports Chaining, Open Addressing & Universal Hashing.Chaining, Open Addressing & Universal Hashing appears earlier in the syllabus and supports AVL Trees: Rotations & Height Maintenance.AVL Trees: Rotations & Height Maintenance appears earlier in the syllabus and supports Adjacency Lists vs. Matrices & BFS.Adjacency Lists vs. Matrices & BFS appears earlier in the syllabus and supports Dijkstra: Correctness & Binary-Heap Implementation.Dijkstra: Correctness & Binary-Heap Implementation appears earlier in the syllabus and supports Floyd-Warshall: Dynamic Programming over Subsets.Floyd-Warshall: Dynamic Programming over Subsets appears earlier in the syllabus and supports Activity Selection & Greedy Exchange Proofs.Activity Selection & Greedy Exchange Proofs appears earlier in the syllabus and supports Max-Flow Min-Cut & Edmonds-Karp.prerequisite

prerequisite relationship

RAM Model, Pseudocode, and Big-O -> Merge-Sort: Algorithm & Analysis

RAM Model, Pseudocode, and Big-O appears earlier in the syllabus and supports Merge-Sort: Algorithm & Analysis.

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RAM Model, Pseudocode, and Big-O

Introduce the random-access machine model and pseudocode conventions used throughout the course.

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RAM Model, Pseudocode, and Big-O: the core idea

Introduce the random-access machine model and pseudocode conventions used throughout the course. The key thing to notice is: RAM model definition (unit-cost memory access, word size, instruction set). A useful example is Instagram feed ranking: O(n log n) vs O(n²) on 2 B photos/day. Do not treat this as a vocabulary item; the point is to use it to reason about a new situation.

Where would RAM Model, Pseudocode, and Big-O show up in an everyday decision or news headline?

Look for the hidden relationship in the example: Instagram feed ranking: O(n log n) vs O(n²) on 2 B photos/day.

RAM Model, Pseudocode, and Big-O: the core idea