Algorithm Design and Analysis
( 24 Modules )

Module #1
Introduction to Algorithm Design and Analysis
Overview of the importance of algorithm design and analysis, and course objectives

Module #2
Asymptotic Notations
Introduction to Big-O, Omega, and Theta notations, and their applications

Module #3
Trade-Offs in Algorithm Design
Discussion of time-space trade-offs, and the importance of considering multiple factors in algorithm design

Module #4
Basic Algorithm Design Techniques
Introduction to brute force, divide-and-conquer, dynamic programming, and greedy algorithm design techniques

Module #5
Greedy Algorithms
In-depth exploration of greedy algorithm design, with examples and case studies

Module #6
Dynamic Programming
In-depth exploration of dynamic programming, with examples and case studies

Module #7
Divide-and-Conquer Algorithms
In-depth exploration of divide-and-conquer algorithm design, with examples and case studies

Module #8
Backtracking Algorithms
Introduction to backtracking algorithm design, with examples and case studies

Module #9
Sorting and Searching
Analysis of popular sorting and searching algorithms, including Bubble Sort, Selection Sort, and Binary Search

Module #10
Graph Traversal Algorithms
Analysis of popular graph traversal algorithms, including DFS and BFS

Module #11
Shortest Paths and Minimum Spanning Trees
Analysis of algorithms for finding shortest paths and minimum spanning trees, including Dijkstras and Bellman-Fords algorithms

Module #12
Network Flow and Matching
Analysis of algorithms for network flow and matching, including Ford-Fulkerson and Hopcroft-Karp algorithms

Module #13
String Matching and Manipulation
Analysis of algorithms for string matching and manipulation, including Rabin-Karp and Knuth-Morris-Pratt algorithms

Module #14
Computational Complexity Theory
Introduction to computational complexity theory, including NP-completeness and reducibility

Module #15
Lower Bound Theory
Analysis of lower bound techniques, including the decision tree model and the adversary method

Module #16
Approximation Algorithms
Introduction to approximation algorithms, including greedy and dynamic programming approaches

Module #17
Online Algorithms
Analysis of online algorithms, including competitive analysis and skyline algorithms

Module #18
Streaming and Sublinear Algorithms
Introduction to streaming and sublinear algorithms, including sampling and sketching techniques

Module #19
Machine Learning and Algorithm Design
Exploration of the relationship between machine learning and algorithm design, including feature engineering and model selection

Module #20
Algorithm Design for Big Data
Discussion of algorithm design considerations for big data, including scalability and parallelism

Module #21
Case Studies in Algorithm Design
In-depth exploration of real-world applications of algorithm design, including compression, encryption, and data compression

Module #22
Ethics in Algorithm Design
Discussion of the ethical implications of algorithm design, including bias, fairness, and transparency

Module #23
Advanced Topics in Algorithm Design
Exploration of advanced topics in algorithm design, including quantum algorithms and homomorphic encryption

Module #24
Course Wrap-Up & Conclusion
Planning next steps in Algorithm Design and Analysis career

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Algorithm Design and Analysis ( 24 Modules )

Algorithm Design and Analysis
( 24 Modules )