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Dynamic Programming, Greedy Algorithms

This course is part of Foundations of Data Structures and Algorithms Specialization

Instructor: Sriram Sankaranarayanan

What you'll learn

Describe basic algorithm design techniques

Create divide and conquer, dynamic programming, and greedy algorithms

Understand intractable problems, P vs NP and the use of integer programming solvers to tackle some of these problems

Skills you'll gain

Data Structures

Data Analysis

Design Strategies

Advanced Mathematics

Mathematical Theory & Analysis

Computational Thinking

Computational Logic

Theoretical Computer Science

Computer Programming

Program Development

Algorithms

Computer Science

Analysis

There are 4 modules in this course

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

Dynamic Programming Algorithms

Greedy Algorithms

Intractability and Supplement on Quantum Computing

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