Algorithms: Design and Analysis, Part 2
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.
Upcoming start dates
- Self-paced Online
Who should attend?
This course is aimed at learners with at least a little bit of programming experience who want to learn the essentials of algorithms. In a University computer science curriculum, this course is typically taken in the third year.
Course delivery details
This course is offered through Stanford University, a partner institute of EdX.
2-4 hours per week
- Verified Track -$149
- Audit Track - Free
Certification / Credits
What you'll learn
- 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
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