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Opiniones y comentarios de aprendices correspondientes a Dynamic Programming, Greedy Algorithms por parte de Universidad de Colorado en Boulder
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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.
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1 - 3 de 3 revisiones para Dynamic Programming, Greedy Algorithms
por Spyros T
•26 de oct. de 2021
i went through this course just for a quick refresh on some basic algorithms and i ended completing all three courses from the specialization! the explanations from Pr.Sriram Sankaranarayanan are crystal clear and the way he presents the material is super! i finnaly understood dynamic programming and P-NP complexity classes, topics that were very comfusing for me. Thank you Proffesor!
por Dave M
•21 de sep. de 2021
Excellent. This course covers some difficult topics, but the lectures and homework assignments were superb and made them quite approachable.
por Rishabh S
•5 de ago. de 2021
Assignment language should be clearly mentioned.
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