Algorithms & Complexity
Computing & Data Science
The mathematical theory of algorithms: asymptotic analysis, sorting, dynamic programming, NP-completeness, randomized algorithms, and network flow.
Learning Objectives
- Analyze algorithm complexity using Big-O and the Master theorem
- Understand comparison sort lower bounds and non-comparison sorts
- Design dynamic programming solutions for optimization problems
- Prove NP-completeness via polynomial reductions
- Analyze randomized algorithms using Chernoff bounds
- Apply amortized analysis to data structures
Lessons
1
Asymptotic Analysis & Recurrences
35 min
2
Sorting Algorithms & Lower Bounds
35 min
3
Dynamic Programming
35 min
4
Greedy Algorithms & Matroids
35 min
5
NP-Completeness & Complexity Theory
35 min
6
Randomized Algorithms
35 min
7
Data Structures & Amortized Analysis
35 min
8
Network Flow Algorithms & Applications
35 min
Quick Practice
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Key Concept Flashcards
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