Algorithms: Design and Analysis, Part 2

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Algorithms: Design and Analysis, Part 2

Course Details

Cost

FREE

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  • TBA

Course Provider

Stanford Online online courses
Stanford Lagunita offers a variety of professional education opportunities in conjunction with many of the University’s schools and departments. We also offer an array of free online courses taught by Stanford faculty to lifelong learners worldwide. We foster collaboration with other education organizations by sharing course material, data-driven research, and source code for enhancements to our open-source platform Stanford Lagunita. We continually experiment to improve what we do throu...
Stanford Lagunita offers a variety of professional education opportunities in conjunction with many of the University’s schools and departments. We also offer an array of free online courses taught by Stanford faculty to lifelong learners worldwide. We foster collaboration with other education organizations by sharing course material, data-driven research, and source code for enhancements to our open-source platform Stanford Lagunita. We continually experiment to improve what we do through creative use of technology, and we share what we learn with the rest of the world.

Provider Subject Specialization
Sciences & Technology
418 reviews

Course Description

Welcome to the self paced course, Algorithms: Design and Analysis, Part 2! Algorithms are the heart of computer science, and the subject has countless practical applications as well as intellectual depth. This course is an introduction to algorithms for learners with at least a little programming experience. The course is rigorous but emphasizes the big picture and conceptual understanding over low-level implementation and mathematical details. After completing this course, you will have a greater mastery of algorithms than almost anyone without a graduate degree in the subject.

Specific topics in Part 2 include: greedy algorithms (scheduling, minimum spanning trees, clustering, Huffman codes), dynamic programming (knapsack, sequence alignment, optimal search trees, shortest paths), NP-completeness and what it means for the algorithm designer, analysis of heuristics, local search.

Learners will practice and master the fun...

Welcome to the self paced course, Algorithms: Design and Analysis, Part 2! Algorithms are the heart of computer science, and the subject has countless practical applications as well as intellectual depth. This course is an introduction to algorithms for learners with at least a little programming experience. The course is rigorous but emphasizes the big picture and conceptual understanding over low-level implementation and mathematical details. After completing this course, you will have a greater mastery of algorithms than almost anyone without a graduate degree in the subject.

Specific topics in Part 2 include: greedy algorithms (scheduling, minimum spanning trees, clustering, Huffman codes), dynamic programming (knapsack, sequence alignment, optimal search trees, shortest paths), NP-completeness and what it means for the algorithm designer, analysis of heuristics, local search.

Learners will practice and master the fundamentals of algorithms through several types of assessments. There are 6 multiple-choice problem sets to test your understanding of the most important concepts. There are also 6 programming assignments, where you implement one of the algorithms covered in lecture in a programming language of your choosing. The course concludes with a multiple-choice final.

There are no assignment due dates and you can work through the course materials and assignments at your own pace.

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