Module #1 Introduction to Algorithm Design and Analysis Overview of the importance of algorithm design and analysis, course objectives, and prerequisites.
Module #2 Asymptotic Notation and Big-O Analysis Introduction to asymptotic notation, Big-O, Omega, and Theta notations, and their applications in algorithm analysis.
Module #3 Time and Space Complexity Analysis Understanding time and space complexity, trade-offs between them, and techniques for analyzing algorithms.
Module #4 Greedy Algorithms Introduction to greedy algorithms, characteristics, and examples (e.g., Huffman coding, activity selection problem).
Module #5 Dynamic Programming Fundamentals of dynamic programming, memoization, and tabulation, with examples (e.g., Fibonacci sequence, longest common subsequence).
Module #6 Divide and Conquer Algorithms Understanding divide and conquer approach, with examples (e.g., binary search, merge sort, fast Fourier transform).
Module #7 Backtracking Algorithms Introduction to backtracking algorithms, with examples (e.g., N-Queens problem, Sudoku solving).
Module #8 Branch and Bound Algorithms Understanding branch and bound approach, with examples (e.g., 0/1 knapsack problem, traveling salesman problem).
Module #9 Graph Algorithms - Introduction Basics of graph theory, graph representation, and graph traversal algorithms (e.g., BFS, DFS).
Module #10 Graph Algorithms - Shortest Paths Algorithms for finding shortest paths in graphs (e.g., Dijkstras, Bellman-Ford, Floyd-Warshall).
Module #11 Graph Algorithms - Minimum Spanning Trees Algorithms for finding minimum spanning trees (e.g., Kruskals, Prims).
Module #18 Cryptography Introduction to cryptography, encryption, decryption, and cryptographic algorithms (e.g., RSA, AES).
Module #19 Algorithm Engineering Practical considerations for implementing algorithms, including data structures, tuning, and profiling.
Module #20 Case Studies in Algorithm Design Real-world examples of algorithm design and analysis, highlighting trade-offs and challenges.
Module #21 Advanced Topics in Algorithm Design Coverage of specialized topics, such as online algorithms, streaming algorithms, or machine learning algorithms.
Module #22 Algorithm Analysis and Lower Bounds Techniques for analyzing algorithms, including lower bounds and trade-off results.
Module #23 Parallel and Distributed Algorithms Algorithms for parallel and distributed computing, including PRAM, BSP, and MapReduce models.
Module #24 Green Algorithms and Sustainable Computing Designing energy-efficient algorithms and exploring sustainable computing practices.
Module #25 Course Wrap-Up & Conclusion Planning next steps in Design and Analysis of Complex Algorithms career
