算法设计与分析 Design and Analysis of Algorithms

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算法设计与分析 Design and Analysis of Algorithms

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Coursera online courses
Coursera's online classes are designed to help students achieve mastery over course material. Some of the best professors in the world - like neurobiology professor and author Peggy Mason from the University of Chicago, and computer science professor and Folding@Home director Vijay Pande - will supplement your knowledge through video lectures. They will also provide challenging assessments, interactive exercises during each lesson, and the opportunity to use a mobile app to keep up with yo...
Coursera's online classes are designed to help students achieve mastery over course material. Some of the best professors in the world - like neurobiology professor and author Peggy Mason from the University of Chicago, and computer science professor and Folding@Home director Vijay Pande - will supplement your knowledge through video lectures. They will also provide challenging assessments, interactive exercises during each lesson, and the opportunity to use a mobile app to keep up with your coursework. Coursera also partners with the US State Department to create “learning hubs” around the world. Students can get internet access, take courses, and participate in weekly in-person study groups to make learning even more collaborative. Begin your journey into the mysteries of the human brain by taking courses in neuroscience. Learn how to navigate the data infrastructures that multinational corporations use when you discover the world of data analysis. Follow one of Coursera’s “Skill Tracks”. Or try any one of its more than 560 available courses to help you achieve your academic and professional goals.

Provider Subject Specialization
Humanities
Sciences & Technology
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Course Description

课程教学目标 针对实际问题需求,进行数学建模并选择高效求解算法的训练,为提高学生的素质和创新能力打下必要的基础。主要内容涉及:面对实际问题建立数学模型、设计正确的求解算法、算法的效率估计、改进算法的途径、问题计算复杂度的估计、难解问题的确定和应对策略等等。本课程是算法课程的基础部分,主要涉及算法的设计、分析与改进途径,其他有关计算复杂性的内容将在后续课程中加以介绍。 课程内容安排 本课程的内容分成两大部分:算法的基础知识、通用算法设计技术与分析方法。 第一部分是算法基础知识,约占20%,主要介绍算法相关的基本概念和数学基础。比如,什么是算法的伪码描述?什么是算法最坏情况下和平均情况下的时间复杂度?算法时间复杂度函数的主要性质,算法复杂度估计中常用的数学方法,如序列求和及递推方程求解。 第二部分是通用的算法设计技术与分析方法,主要介绍分治策略、动态规划、贪心法、回溯与分支限界。主要介绍这些设计技术的使用条件、分析方法、改进途径,并给出一些重要的应用。
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