Course Description

Dynamic programming is a powerful technique for solving optimization problems that involve making a sequence of decisions. This course will provide an in-depth introduction to dynamic programming and its applications. The course will begin with an overview of the basic concepts and principles of dynamic programming, including the principles of optimality, subproblems, and memoization. From there, the course will cover a range of specific dynamic programming algorithms, including the knapsack problem, the longest common subsequence problem, and the Bellman-Ford algorithm for shortest paths. Throughout the course, students will learn how to design and analyze dynamic programming algorithms, as well as how to implement them in code. The course will provide a mix of theoretical and practical material, including proofs of correctness and complexity analysis, as well as hands-on programming assignments. By the end of the course, students will have a solid understanding of the principles and applications, as well as the ability to apply these techniques to solve a wide range of optimization problems. They will also have experience implementing dynamic programming algorithms in code, and will be able to apply these skills in their own projects and research. The course is suitable for students with a background in algorithms and data structures, as well as for professionals looking to expand their skills in optimization and programming. It will provide a strong foundation for further study in related areas, such as machine learning, artificial intelligence, and operations research. Overall, this course will provide students with a deep understanding and its applications, and will equip them with the skills they need to apply these techniques in their own work. Whether you are a student, a researcher, or a professional, this course will provide you with the tools you need to solve complex optimization problems. Author: Gaurav Sen