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Learner Reviews & Feedback for Dynamic Programming, Greedy Algorithms by University of Colorado Boulder

4.4
stars
32 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. Dynamic Programming, Greedy Algorithms can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder....

Top reviews

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).

DM

Sep 20, 2021

Excellent. This course covers some difficult topics, but the lectures and homework assignments were superb and made them quite approachable.

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