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
