Algorithms: Design and Analysis - 2
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
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.
This is the 1st course in the intermediate, undergraduate-level offering that makes up the larger Algorithms MicroBachelors program. We recommend taking them in order, unless you have a background in these areas already and feel comfortable skipping ahead.
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