This course covers basic algorithm design techniques such as divide and conquer, dynamic programming, and greedy algorithms. It concludes with a brief introduction to intractability (NP-completeness) and using linear/integer programming solvers for solving optimization problems. We will also cover some advanced topics in data structures.Lee mas.
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The techniques and tools covered in Dynamic Programming, Greedy Algorithms are most similar to the requirements found in Científico de datos data science job advertisements.
Dynamic Programming, Greedy Algorithms is a part of uno structured learning path.
3 Courses
3 Months
Data Science Foundations: Data Structures and Algorithms
