Back to Dynamic Programming, Greedy Algorithms
Learner Reviews & Feedback for Dynamic Programming, Greedy Algorithms by University of Colorado Boulder
174 ratings
About the Course
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.
This course can be taken for academic credit as part of CU Boulder’s MS in Data Science or MS in Computer Science degrees offered on the Coursera platform. These fully accredited graduate degrees offer targeted courses, short 8-week sessions, and pay-as-you-go tuition. Admission is based on performance in three preliminary courses, not academic history. CU degrees on Coursera are ideal for recent graduates or working professionals. Learn more:
MS in Data Science: https://www.coursera.org/degrees/master-of-science-data-science-boulder
MS in Computer Science: https://coursera.org/degrees/ms-computer-science-boulder
Top reviews
BC
Dec 6, 2022
This course save me time on learning the dynamic programming. I really love the 4-steps to construct the dynamic programming. It gives me the guideline when designing DP solution.
AM
Sep 18, 2022
Great work from professor Sriram Sankaranarayanan explaining such complex material. I wish we could review more examples during the class (specially Dynamic Programming ones).
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